TNF-α variants

ABSTRACT

The invention relates to novel proteins with TNF-α antagonist activity and nucleic acids encoding these proteins. The invention further relates to the use of the novel proteins in the treatment of TNF-α related disorders, such as rheumatoid arthritis.

This application is a continuing application of U.S. Ser. No.60/186,427, filed Mar. 2, 2000.

FIELD OF THE INVENTION

The invention relates to novel proteins with TNF-α antagonist activityand nucleic acids encoding these proteins. The invention further relatesto the use of the novel proteins in the treatment of TNF-α relateddisorders, such as rheumatoid arthritis.

BACKGROUND OF THE INVENTION

Tumor necrosis factor a (TNF-α) is a pleiotropic cytokine that isprimarily produced by activated macrophages and lymphocytes; but is alsoexpressed in endothelial cells and other cell types. TNF-α is a majormediator of inflammatory, immunological, and pathophysiologicalreactions. (Grell, M., et al., (1995) Cell, 83:793–802). Two distinctforms of TNF exist, a 26 kDa membrane expressed form and the soluble 17kDa cytokine which is derived from proteolytic cleavage of the 26 kDaform. The soluble TNF polypeptide is 157 amino acids long and is theprimary biologically active molecule.

TNF-α exerts its biological effects through interaction withhigh-affinity cell surface receptors. Two distinct membrane TNF-αreceptors have been cloned and characterized. These are a 55 kDaspecies, designated p55 TNF-R and a 75 kDa species designated p75 TNF-R(Corcoran. A. E., et al., (1994) Eur. J. Biochem., 223:831–840). The twoTNF receptors exhibit 28% similarity at the amino acid level. This isconfined to the extracellular domain and consists of four repeatingcysteine-rich motifs, each of approximately 40 amino acids. Each motifcontains four to six cysteines in conserved positions. Dayhoff analysisshows greatest intersubunit similarity among the first three repeats ineach receptor. This characteristic structure is shared with a number ofother receptors and cell surface molecules which comprise theTNF-R/nerve growth factor receptor superfamily (Corcoran. A. E., et al.,(1994) Eur. J. Biochem., 223:831–840).

TNF signaling is initiated by receptor clustering, either by thetrivalent ligand TNF or by cross-linking monoclonal antibodies(Vandevoorde, V., et al., (1997) J. Cell Biol., 137:1627–1638).Crystallographic studies of TNF and the structurally related cytokine,lymphotoxin (LT) have shown that both cytokines exist as homotrimers,with subunits packed edge to edge in a threefold symmetry. Structurally,neither TNF or LT reflect the repeating pattern of the their receptors.Each monomer is cone shaped and contains two hydrophilic loops onopposite sides of the base of the cone. Recent crystallization of a p55soluble TNF-R/LT complex has confirmed the hypothesis that loops fromadjacent monomers join together to form a groove between monomers andthat TNF-R binds in these grooves (Corcoran. A. E., et al., (1994) Eur.J. Biochem., 223:831–840).

The key role played by TNF-α in inflammation, cellular immune responsesand the pathology of many diseases has led to the search for antagonistsof TNF-α. Soluble TNF receptors which interfere with TNF-α signalinghave been isolated and are marketed by Immunex as Enbrel® for thetreatment of rheumatoid arthritis. Random mutagenesis has been used toidentify active sites in TNF-α responsible for the loss of cytotoxicactivity (Van Ostade, X., et al., (1991) EMBO J., 10:827–836). However,a need still exists to develop more potent TNF-α antagonists for use astherapeutic agents.

Accordingly, it is an object of the invention to provide proteins withTNF-α antagonist activity and nucleic acids encoding these proteins forthe treatment of TNF-α related disorders.

SUMMARY OF THE INVENTION

In accordance with the objects outlined above, the present inventionprovides non-naturally occurring variant TNF-α proteins (e.g. proteinsnot found in nature) comprising amino acid sequences with at least oneamino acid change compared to the wild-type TNF-α proteins. Preferredembodiments utilize variant TNF-α proteins that preferentially interactwith the wild-type TNF-α to form mixed trimers incapable of activatingreceptor signaling. Preferably, variant TNF-α proteins with 1, 2, 3, 4,and 5 amino acid changes are used as compared to wild-type TNF-αprotein. In a preferred embodiment these changes are selected frompositions 21, 30, 31, 32, 33, 35, 65, 66, 67, 111, 112, 115, 140, 143,144, 146 and 147.

In an additional aspect, the non-naturally occurring variant TNF-αproteins have substitutions selected from the group of substitutionsconsisting of D143E, D143N, D143S, A145R, A145K, A145E, E146K, E146R andA84V.

In a further aspect, the invention provides recombinant nucleic acidsencoding the non-naturally occurring variant TNF-α proteins, expressionvectors, and host cells.

In an additional aspect, the invention provides methods of producing anon-naturally occurring variant TNF-α protein comprising culturing thehost cell of the invention under conditions suitable for expression ofthe nucleic acid.

In a further aspect, the invention provides pharmaceutical compositionscomprising a variant TNF-α protein of the invention and a pharmaceuticalcarrier.

In a further aspect, the invention provides methods for treating anTNF-α related disorder comprising administering a variant TNF-α proteinof the invention to a patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the design strategy for TNF-α mutants.

FIG. 1A depicts a complex of TNF receptor with wild-type TNF-α.

FIG. 1B depicts a mixed trimer of mutant TNF-α (TNF-X) and wild-typeTNF-α. Dark circles are receptor molecules, light pentagons arewild-type TNF-α and the dark pentagon is a mutant TNF-α.

FIG. 2 depicts the structure of the wild-type TNF-TNF-R trimer complex.

FIG. 3 depicts the structure of the p55 TNF-R extra-cellular domain. Thedarker appear regions represent residues required for contact withTNF-α.

FIG. 4 depicts the binding sites on TNF-α that are involved in bindingthe TNF-R.

FIG. 5 depicts the TNF-α trimer interface.

FIG. 6A (SEQ NO:1) depicts the nucleotide sequence of the histidinetagged wild-type TNF-α molecule used as a template molecule form whichthe mutants were generated. The additional 6 histidines, located betweenthe start codon and the first amino acid are underlined.

FIG. 6B (SEQ ID NO:2) depicts the amino acid sequence of wild-type TNF-αwith an additional 6 histidines (underlined) between the start codon andthe first amino acid. Amino acids changed in the TNF-α mutants are shownin bold.

FIG. 7 depicts the position and the amino acid changes in the TNF-αmutants (SEQ ID NOS:23–44).

FIG. 8 depicts the % TNF-α activity of the mutants listed in FIG. 7. The“oligo name” is based on the changed amino acid in the mutant and theposition where the change was generated.

FIGS. 9A and B depict the reproducibility of the TNF-α activity of themutants.

FIG. 10 depicts the mutation pattern of TNF-α protein sequences. Theprobability table shows only the amino acid residues of positions 72,73, 75, 86, 87, 97 and 137. The occurrence of each amino acid residue ata given position is indicated as a relative probability. For example, atposition 137, the wild-type amino acid is asparagine; in the TNF-αvariants, aspartic is the preferred amino acid at this position.

FIG. 11 depicts another example of the mutation pattern of TNF-α proteinsequences. The probability table shows only the amino acid residues ofpositions 21, 30, 31, 32, 33, 35, 65, 66, 67, 111, 112, 115, 140,143,144, 145, 146 and 147. The occurrence of each amino acid residue at agiven position is indicated as a relative probability. For example, atposition 21, the wild-type amino acid is glutamine; in the TNF-αvariants, arginine is the preferred amino acid at this position.

FIG. 12 (SEQ ID NO:3–8) depicts trimerization domains from TRAFproteins.

FIG. 13 depicts the synthesis of a full-length gene and all possiblemutations by PCR. Overlapping oligonucleotides corresponding to thefull-length gene (black bar, Step 1) and comprising one or more desiredmutations are synthesized, heated and annealed. Addition of DNApolymerase to the annealed oligonucleotides results in the 5′ to 3′synthesis of DNA (Step 2) to produce longer DNA fragments (Step 3).Repeated cycles of heating, annealing, and DNA synthesis (Step 4) resultin the production of longer DNA, including some full-length molecules.These can be selected by a second round of PCR using primers (indicatedby arrows) corresponding to the end of the full-length gene (Step 5).

FIG. 14 depicts a preferred method for synthesizing a library of thevariant TNF-α proteins of the invention using the wild type gene.

FIG. 15 depicts an overlapping extension method. At the top of FIG. 15Ais the template DNA showing the locations of the regions to be mutated(black boxes) and the binding sites of the relevant primers (arrows).The primers R1 and R2 represent a pool of primers, each containing adifferent mutation; as described herein, this may be done usingdifferent ratios of primers if desired. The variant position is flankedby regions of homology sufficient to get hybridization. In this example,three separate PCR reactions are done for step 1. The first reactioncontains the template plus oligos F1 and R1. The second reactioncontains template plus F2 and R2, and the third contains the templateand F3 and R3. The reaction products are shown. In Step 2, the productsfrom Step 1 tube 1 and Step 1 tube 2 are taken. After purification awayfrom the primers, these are added to a fresh PCR reaction together withF1 and R4. During the denaturation phase of the PCR, the overlappingregions anneal and the second strand is synthesized. The product is thenamplified by the outside primers. In Step 3, the purified product fromStep 2 is used in a third PCR reaction, together with the product ofStep 1, tube 3 and the primers F1 and R3. The final product correspondsto the full length gene and contains the required mutations.

FIG. 16 depicts a ligation of PCR reaction products to synthesize thelibraries of the invention. In this technique, the primers also containan endonuclease restriction site (RE), either blunt, 5′ overhanging or3′ overhanging. We set up three separate PCR reactions for Step 1. Thefirst reaction contains the template plus oligos F1 and R1. The secondreaction contains template plus F2 and R2, and the third contains thetemplate and F3 and R3. The reaction products are shown. In Step 2, theproducts of step 1 are purified and then digested with the appropriaterestriction endonuclease. The digestion products from Step 2, tube 1 andStep 2, tube 2 and ligate them together with DNA ligase (step 3). Theproducts are then amplified in Step 4 using primer F1 and R4. The wholeprocess is then repeated by digesting the amplified products, ligatingthem to the digested products of Step 2, tube 3, and amplifying thefinal product by primers F1 and R3. It would also be possible to ligateall three PCR products from Step 1 together in one reaction, providingthe two restriction sites (RET and RE2) were different.

FIG. 17 depicts blunt end ligation of PCR products. In this technique,the primers such as F1 and R1 do not overlap, but they abut. Again threeseparate PCR reactions are performed. The products from tube 1 and tube2 are ligated, and then amplified with outside primers F1 and R4. Thisproduct is then gated with the product from Step 1, tube 3. The finalproducts are then amplified with primers F1 and R3.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to novel proteins and nucleic acidspossessing TNF-α antagonist activity. The proteins are generated using asystem previously described in W098/47089 and U.S. Ser. Nos. 09/058,459,09/127,926, 60/104,612, 60/158,700, 09/419,351, 601181,630, 60/186,904,09/419,351, and an application entitled “Protein Design Automation forProtein Libraries”, filed Feb. 12, 2001 (no U.S. serial number receivedyet) all of which are expressly incorporated by reference in theirentirety. In general, these applications describe a variety ofcomputational modeling systems that allow the generation of extremelystable proteins. In this way, variants of TNF proteins are generatedthat act as antagonists for wild-type TNF-α. Variant TNF-proteins may begenerated from wild-type TNF-α, p55 TNF-R and p75 TNF-R proteins, withpreferred embodiments including variant TNF-α proteins.

Generally, there are a variety of computational methods that can be usedto generate the variant TNF proteins of the invention. In a preferredembodiment, sequence based methods are used. Alternatively, structurebased methods, such as PDA, described in detail below, are used.

Similarly, molecular dynamics calculations can be used tocomputationally screen sequences by individually calculating mutantsequence scores and compiling a rank ordered list.

In a preferred embodiment, residue pair potentials can be used to scoresequences (Miyazawa et al., Macromolecules 18(3):534–552 (1985),expressly incorporated by reference) during computational screening.

In a preferred embodiment, sequence profile scores (Bowie et al.,Science 253(5016):164–70 (1991), incorporated by reference) and/orpotentials of mean force (Hendlich et al., J. Mol. Biol. 216(1):167–180(1990), also incorporated by reference) can also be calculated to scoresequences. These methods assess the match between a sequence and a 3Dprotein structure and hence can act to screen for fidelity to theprotein structure. By using different scoring functions to ranksequences, different regions of sequence space can be sampled in thecomputational screen.

Furthermore, scoring functions can be used to screen for sequences thatwould create metal or co-factor binding sites in the protein (Hellinga,Fold Des. 3(1):R1–8 (1998), hereby expressly incorporated by reference).Similarly, scoring functions can be used to screen for sequences thatwould create disulfide bonds in the protein. These potentials attempt tospecifically modify a protein structure to introduce a new structuralmotif.

In a preferred embodiment, sequence and/or structural alignment programscan be used to generate the variant TNF-α proteins of the invention. Asis known in the art, there are a number of sequence-based alignmentprograms; including for example, Smith-Waterman searches,Needleman-Wunsch, Double Affine Smith-Waterman, frame search,Gribskov/GCG profile search, GribskovlGCG profile scan, profile framesearch, Bucher generalized profiles, Hidden Markov models, Hframe,Double Frame, Blast, Psi-Blast, Clustal, and GeneWise.

As is known in the art, there are a number of sequence alignmentmethodologies that can be used. For example, sequence homology basedalignment methods can be used to create sequence alignments of proteinsrelated to the target structure (Altschul et al., J. Mol. Biol.215(3):403–410 (1990), Altschul et al., Nucleic Acids Res. 25:3389–3402(1997), both incorporated by reference). These sequence alignments arethen examined to determine the observed sequence variations. Thesesequence variations are tabulated to define a set of variant TNF-αproteins. Sequence based alignments can be used in a variety of ways.For example, a number of related proteins can be aligned, as is known inthe art, and the “variable” and “conserved” residues defined; that is,the residues that vary or remain identical between the family memberscan be defined. These results can be used to generate a probabilitytable, as outlined below. Similarly, these sequence variations can betabulated and a secondary library defined from them as defined below.Alternatively, the allowed sequence variations can be used to define theamino acids considered at each position during the computationalscreening. Another variation is to bias the score for amino acids thatoccur in the sequence alignment, thereby increasing the likelihood thatthey are found during computational screening but still allowingconsideration of other amino acids. This bias would result in a focusedlibrary of variant TNF-α proteins but would not eliminate fromconsideration amino acids not found in the alignment. In addition, anumber of other types of bias may be introduced. For example, diversitymay be forced; that is, a “conserved” residue is chosen and altered toforce diversity on the protein and thus sample a greater portion of thesequence space. Alternatively, the positions of high variability betweenfamily members (i.e. low conservation) can be randomized, either usingall or a subset of amino acids. Similarly, outlier residues, eitherpositional outliers or side chain outliers, may be eliminated.

Similarly, structural alignment of structurally related proteins can bedone to generate sequence alignments (Orengo et al., Structure5(8):1093–108 (1997); Holm et al., Nucleic Acids Res. 26(1):3 (1998),both of which are incorporated by reference). These sequence alignmentscan then be examined to determine the observed sequence variations.Libraries can be generated by predicting secondary structure fromsequence, and then selecting sequences that are compatible with thepredicted secondary structure. There are a number of secondary structureprediction methods such as helix-coil transition theory (Munoz andSerrano, Biopolymers 41:495, 1997), neural networks, local structurealignment and others (e.g., see in Selbig et al., Bioinformatics15:1039–46, 1999).

Similarly, as outlined above, other computational methods are known,including, but not limited to, sequence profiling [Bowie and Eisenberg,Science 253(5016):164–70, (1991)], rotamer library selections [Dahiyatand Mayo, Protein Sci. 5(5):895–903 (1996); Dahiyat and Mayo, Science278(5335):82-7 (1997); Desjarlais and Handel, Protein Science4:2006–2018 (1995); Harbury et al Proc. Natl. Acad. Sci. U.S.A.92(18):8408–8412 (1995); Kono et al., Proteins: Structure, Function andGenetics 19:244–255 (1994); Hellinga and Richards, Proc. Natl. Acad.Sci. U.S.A. 91:5803–5807 (1994)]; and residue pair potentials [Jones,Protein Science 3:567–574, (1994)]; PROSA [Heindlich et al., J. Mol.Biol. 216:167–180 (1990)]; THREADER [Jones et al., Nature 358:86–89(1992)], and other inverse folding methods such as those described bySimons et al. [Proteins, 34:535–543, (1999)], Levitt and Gerstein [Proc.Natl. Acad. Sci. U.S.A., 95:5913–5920, (1998)], Godzik and Skolnick[Proc. Natl. Acad. Sci. U.S.A., 89:12098–102, (1992)], Godzik et al. [J.Mol. Biol. 227:227–38, (1992)] an profile methods [Gribskov et al. Proc.Natl. Acad. Sci. U.S.A. 84:4355–4358 (1987) and Fischer and Eisenberg,Protein Sci. 5:947–955 (1996), Rice and Eisenberg J. Mol. Biol.267:1026–1038(1997)], all of which are expressly incorporated byreference. In addition, other computational methods such as thosedescribed by Koehl and Levitt (J. Mol. Biol. 293:1161–1181 (1999); J.Mol. Biol. 293:1183–1193 (1999); expressly incorporated by reference)can be used to create a variant TNF-α library which can optionally thenbe used to generate a smaller secondary library for use in experimentalscreening for improved properties and function. In addition, there arecomputational methods based on forcefield calculations such as SCMF thatcan be used as well for SCMF, see Delarue et al. Pac. Symp. Biocomput.109–21 (1997); Koehl et al., J. Mol. Biol. 239:249–75 (1994); Koehl etal., Nat. Struct. Biol. 2:163–70 (1995); Koehl et al., Curr. Opin.Struct. Biol. 6:222–6 (1996); Koehl et al., J. Mol. Biol. 293:1183–93(1999); Koehl etal., J. Mol. Biol. 293:1161–81 (1999); Lee J., Mol.Biol. 236:918–39 (1994); and Vasquez Biopolymers 36:53–70 (1995); all ofwhich are expressly incorporated by reference. Other forcefieldcalculations that can be used to optimize the conformation of a sequencewithin a computational method, or to generate de novo optimizedsequences as outlined herein include, but are not limited to, OPLS-M[Jorgensen et al., J. Am. Chem. Soc. 118:11225–11236 (1996); Jorgensen,W. L.; BOSS, Version 4.1; Yale University: New Haven, Conn. (1999)];OPLS [Jorgensen et al., J. Am. Chem. Soc.1 10:1657ff (1988); Jorgensenet al., J Am. Chem. Soc.1 12:4768ff (1990)]; UNRES (United ResidueForcefield; Liwo et al., Protein Science 2:1697–1714 (1993); Liwo etal., Protein Science 2:1715–1731 (1993); Liwo et al., J. Comp. Chem.18:849–873 (1997); Liwo et al., J. Comp. Chem. 18:874–884 (1997); Liwoetal., J. Comp. Chem. 19:259–276 (1998); Forcefield for ProteinStructure Prediction (Liwo et al., Proc. Natl. Acad. Sci. U.S.A.96:5482–5485 (1999)]; ECEPP/3 [Liwo et al., J Protein Chem. 13(4):375–80(1994)]; AMBER 1.1 force field (Weiner et al., Am. Chem. Soc.106:765–784); AMBER 3.0 force field [U.C. Singh et al., Proc. Natl.Acad. Sci. U.S.A. 82:755–759 (1985)]; CHARMM and CHARMM22 (Brooks etal., J. Comp. Chem. 4:187–217); cvff3.0 [Dauber-Osguthorpe et al.,Proteins: Structure, Function and Genetics, 4:31–47 (1988)]; cff9l(Maple et al., J. Comp. Chem. 15:162–182); also, the DISCOVER (cvff andcff91) and AMBER forcefields are used in the INSIGHT molecular modelingpackage (Biosym/MSI, San Diego Calif.) and HARMM is used in the QUANTAmolecular modeling package (Biosym/MSI, San Diego Calif.), all of whichare expressly incorporated by reference. In fact, as is outlined below,these forcefield methods may be used to generate the variant TNF-αlibrary directly; these methods can be used to generate a probabilitytable from which an additional library is directly generated.

In a preferred embodiment, the computational method used to generate theset or library of variant TNF-α proteins is Protein Design Automation(PDA), as is described in U.S. Ser. Nos 60/061,097, 60/043,464,60/054,678, 09/127,926, 60/104,612, 60/158,700, 09/419,351, 60/181630,60/186,904, 09/419,351, and an application entitled “Protein DesignAutomation for Protein Libraries”, filed Feb. 12, 2001 (no U.S. serialnumber received yet) and PCT US98/07254, all of which are expresslyincorporated herein by reference. Briefly, PDA can be described asfollows. A known protein structure is used as the starting point. Theresidues to be optimized are then identified, which may be the entiresequence or subset(s) thereof. The side chains of any positions to bevaried are then removed. The resulting structure consisting of theprotein backbone and the remaining sidechains is called the template.Each variable residue position is then preferably classified as a coreresidue, a surface residue, or a boundary residue; each classificationdefines a subset of possible amino acid residues for the position (forexample, core residues generally will be selected from the set ofhydrophobic residues, surface residues generally will be selected fromthe hydrophilic residues, and boundary residues may be either). Eachamino acid can be represented by a discrete set of all allowedconformers of each side chain, called rotamers. Thus, to arrive at anoptimal sequence for a backbone, all possible sequences of rotamers mustbe screened, where each backbone position can be occupied either by eachamino acid in all its possible rotameric states, or a subset of aminoacids, and thus a subset of rotamers.

Two sets of interactions are then calculated for each rotamer at everyposition: the interaction of the rotamer side chain with all or part ofthe backbone (the “singles” energy, also called the rotamer/template orrotamer/backbone energy), and the interaction of the rotamer side chainwith all other possible rotamers at every other position or a subset ofthe other positions (the “doubles” energy, also called therotamer/rotamer energy). The energy of each of these interactions iscalculated through the use of a variety of scoring functions, whichinclude the energy of van der Waal's forces, the energy of hydrogenbonding, the energy of secondary structure propensity, the energy ofsurface area salvation and the electrostatics. Thus, the total energy ofeach rotamer interaction, both with the backbone and other rotamers, iscalculated, and stored in a matrix form.

The discrete nature of rotamer sets allows a simple calculation of thenumber of rotamer sequences to be tested. A backbone of length n with mpossible rotamers per position will have m^(n) possible rotamersequences, a number which grows exponentially with sequence length andrenders the calculations either unwieldy or impossible in real time.Accordingly, to solve this combinatorial search problem, a “Dead EndElimination” (DEE) calculation is performed. The DEE calculation isbased on the fact that if the worst total interaction of a first rotameris still better than the best total interaction of a second rotamer,then the second rotamer cannot be part of the global optimum solution.Since the energies of all rotamers have already been calculated, the DEEapproach only requires sums over the sequence length to test andeliminate rotamers, which speeds up the calculations considerably. DEEcan be rerun comparing pairs of rotamers, or combinations of rotamers,which will eventually result in the determination of a single sequencewhich represents the global optimum energy.

Once the global solution has been found, a Monte Carlo search may bedone to generate a rank-ordered list of sequences in the neighborhood ofthe DEE solution. Starting at the DEE solution, random positions arechanged to other rotamers, and the new sequence energy is calculated. Ifthe new sequence meets the criteria for acceptance, it is used as astarting point for another jump. After a predetermined number of jumps,a rank-ordered list of sequences is generated. Monte Carlo searching isa sampling technique to explore sequence space around the global minimumor to find new local minima distant in sequence space. As is moreadditionally outlined below, there are other sampling techniques thatcan be used, including Boltzman sampling, genetic algorithm techniquesand simulated annealing. In addition, for all the sampling techniques,the kinds of jumps allowed can be altered (e.g. random jumps to randomresidues, biased jumps (to or away from wild-type, for example), jumpsto biased residues (to or away from similar residues, for example),etc.). Similarly, for all the sampling techniques, the acceptancecriteria of whether a sampling jump is accepted can be altered.

As outlined in U.S. Ser. No. 09/127,926, the protein backbone(comprising (for a naturally occurring protein) the nitrogen, thecarbonyl carbon, the a-carbon, and the carbonyl oxygen, along with thedirection of the vector from the α-carbon to the β-carbon) may bealtered prior to the computational analysis, by varying a set ofparameters called supersecondary structure parameters.

Once a protein structure backbone is generated (with alterations, asoutlined above) and input into the computer, explicit hydrogens areadded if not included within the structure (for example, if thestructure was generated by X-ray crystallography, hydrogens must beadded). After hydrogen addition, energy minimization of the structure isrun, to relax the hydrogens as well as the other atoms, bond angles andbond lengths. In a preferred embodiment, this is done by doing a numberof steps of conjugate gradient minimization [Mayo et al., J. Phys. Chem.94:8897 (1990)] of atomic coordinate positions to minimize the Dreidingforce field with no electrostatics. Generally from about 10 to about 250steps is preferred, with about 50 being most preferred.

The protein backbone structure contains at least one variable residueposition. As is known in the art, the residues, or amino acids, ofproteins are generally sequentially numbered starting with theN-terminus of the protein. Thus a protein having a methionine at it'sN-terminus is said to have a methionine at residue or amino acidposition 1, with the next residues as 2, 3, 4, etc. At each position,the wild type (i.e. naturally occurring) protein may have one of atleast 20 amino acids, in any number of rotamers. By “variable residueposition” herein is meant an amino acid position of the protein to bedesigned that is not fixed in the design method as a specific residue orrotamer, generally the wild-type residue or rotamer.

In a preferred embodiment, all of the residue positions of the proteinare variable. That is, every amino acid side chain may be altered in themethods of the present invention. This is particularly desirable forsmaller proteins, although the present methods allow the design oflarger proteins as well. While there is no theoretical limit to thelength of the protein which may be designed this way, there is apractical computational limit.

In an alternate preferred embodiment, only some of the residue positionsof the protein are variable, and the remainder are “fixed”, that is,they are identified in the three dimensional structure as being in a setconformation. In some embodiments, a fixed position is left in itsoriginal conformation (which may or may not correlate to a specificrotamer of the rotamer library being used). Alternatively, residues maybe fixed as a non-wild type residue; for example, when knownsite-directed mutagenesis techniques have shown that a particularresidue is desirable (for example, to eliminate a proteolytic site oralter the substrate specificity of an enzyme), the residue may be fixedas a particular amino acid. Alternatively, the methods of the presentinvention may be used to evaluate mutations de novo, as is discussedbelow. In an alternate preferred embodiment, a fixed position may be“floated”; the amino acid at that position is fixed, but differentrotamers of that amino acid are tested. In this embodiment, the variableresidues may be at least one, or anywhere from 0.1% to 99.9% of thetotal number of residues. Thus, for example, it may be possible tochange only a few (or one) residues, or most of the residues, with allpossibilities in between.

In a preferred embodiment, residues which can be fixed include, but arenot limited to, structurally or biologically functional residues;alternatively, biologically functional residues may specifically not befixed. For example, residues which are known to be important forbiological activity, such as the residues which the binding site for abinding partner (ligand/receptor, antigen/antibody, etc.),phosphorylation or glycosylation sites which are crucial to biologicalfunction, or structurally important residues, such as disulfide bridges,metal binding sites, critical hydrogen bonding residues, residuescritical for backbone conformation such as proline or glycine, residuescritical for packing interactions, etc. may all be fixed in aconformation or as a single rotamer, or “floated”.

Similarly, residues which may be chosen as variable residues may bethose that confer undesirable biological attributes, such assusceptibility to proteolytic degradation, dimerization or aggregationsites, glycosylation sites which may lead to immune responses, unwantedbinding activity, unwanted allostery, undesirable enzyme activity butwith a preservation of binding, etc. In the present invention, it is theoligomerization domain residues which are varied, as outlined below.

In a preferred embodiment, each variable position is classified aseither a core, surface or boundary residue position, although in somecases, as explained below, the variable position may be set to glycineto minimize backbone strain. In addition, as outlined herein, residuesneed not be classified, they can be chosen as variable and any set ofamino acids may be used. Any combination of core, surface and boundarypositions can be utilized: core, surface and boundary residues; core andsurface residues; core and boundary residues, and surface and boundaryresidues, as well as core residues alone, surface residues alone, orboundary residues alone.

The classification of residue positions as core, surface or boundary maybe done in several ways, as will be appreciated by those in the art. Ina preferred embodiment, the classification is done via a visual scan ofthe original protein backbone structure, including the side chains, andassigning a classification based on a subjective evaluation of oneskilled in the art of protein modeling. Alternatively, a preferredembodiment utilizes an assessment of the orientation of the Cα–Cβvectors relative to a solvent accessible surface computed using only thetemplate Cα atoms, as outlined in U.S. Ser. Nos 60/061,097, 60/043,464,60/054,678, 09/127,926 60/104,612, 60/158,700, 09/419,351, 60/181630,60/186,904, 09/419,351 and an application entitled “Protein DesignAutomation for Protein Libraries” filed Feb. 12, 2001 (no U.S. serialnumber received yet) and PCT US98/07254. Alternatively, a surface areacalculation can be done.

Once each variable position is classified as either core, surface orboundary, a set of amino acid side chains, and thus a set of rotamers,is assigned to each position. That is, the set of possible amino acidside chains that the program will allow to be considered at anyparticular position is chosen. Subsequently, once the possible aminoacid side chains are chosen, the set of rotamers that will be evaluatedat a particular position can be determined. Thus, a core residue willgenerally be selected from the group of hydrophobic residues consistingof alanine, valine, isoleucine, leucine, phenylalanine, tyrosine,tryptophan, and methionine (in some embodiments, when the a scalingfactor of the van der Waals scoring function, described below, is low,methionine is removed from the set), and the rotamer set for each coreposition potentially includes rotamers for these eight amino acid sidechains (all the rotamers if a backbone independent library is used, andsubsets if a rotamer dependent backbone is used). Similarly, surfacepositions are generally selected from the group of hydrophilic residuesconsisting of alanine, serine, threonine, aspartic acid, asparagine,glutamine, glutamic acid, arginine, lysine and histidine. The rotamerset for each surface position thus includes rotamers for these tenresidues. Finally, boundary positions are generally chosen from alanine,serine, threonine, aspartic acid, asparagine, glutamine, glutamic acid,arginine, lysine histidine, valine, isoleucine, leucine, phenylalanine,tyrosine, tryptophan, and methionine. The rotamer set for each boundaryposition thus potentially includes every rotamer for these seventeenresidues (assuming cysteine, glycine and proline are not used, althoughthey can be). Additionally, in some preferred embodiments, a set of 18naturally occurring amino acids (all except cysteine and proline, whichare known to be particularly disruptive) are used.

Thus, as will be appreciated by those in the art, there is acomputational benefit to classifying the residue positions, as itdecreases the number of calculations. It should also be noted that theremay be situations where the sets of core, boundary and surface residuesare altered from those described above; for example, under somecircumstances, one or more amino acids is either added or subtractedfrom the set of allowed amino acids. For example, some proteins whichdimerize or multimerize, or have ligand binding sites, may containhydrophobic surface residues, etc. In addition, residues that do notallow helix “capping” or the favorable interaction with an α-helixdipole may be subtracted from a set of allowed residues. Thismodification of amino acid groups is done on a residue by residue basis.

In a preferred embodiment, proline, cysteine and glycine are notincluded in the list of possible amino acid side chains, and thus therotamers for these side chains are not used. However, in a preferredembodiment, when the variable residue position has a φ angle (that is,the dihedral angle defined by 1) the carbonyl carbon of the precedingamino acid; 2) the nitrogen atom of the current residue; 3) the α-carbonof the current residue; and 4) the carbonyl carbon of the currentresidue) greater than 0°, the position is set to glycine to minimizebackbone strain.

Once the group of potential rotamers is assigned for each variableresidue position, processing proceeds as outlined in U.S. Ser. No.091127,926 and PCT US98/07254. This processing step entails analyzinginteractions of the rotamers with each other and with the proteinbackbone to generate optimized protein sequences. Simplistically, theprocessing initially comprises the use of a number of scoring functionsto calculate energies of interactions of the rotamers, either to thebackbone itself or other rotamers. Preferred PDA scoring functionsinclude, but are not limited to, a Van der Waals potential scoringfunction, a hydrogen bond potential scoring function, an atomicsalvation scoring function, a secondary structure propensity scoringfunction and an electrostatic scoring function. As is further describedbelow, at least one scoring function is used to score each position,although the scoring functions may differ depending on the positionclassification or other considerations, like favorable interaction withan α-helix dipole. As outlined below, the total energy which is used inthe calculations is the sum of the energy of each scoring function usedat a particular position, as is generally shown in Equation 1:

Equation 1:E _(total) =nE _(vdw) +nE _(as) +nE _(h-bonding) +nE _(ss) +nE _(elec)In Equation 1, the total energy is the sum of the energy of the van derWaals potential (E_(vdw)), the energy of atomic salvation (E_(as)), theenergy of hydrogen bonding (E_(h-bonding)), the energy of secondarystructure (E_(ss)) and the energy of electrostatic interaction(E_(elec)). The term n is either 0 or 1, depending on whether the termis to be considered for the particular residue position.

As outlined in U.S. Ser. Nos. 60/061,097, 60/043,464, 60/054,678,09/127,926, 60/104,612, 60/158,700, 09/419,351, 60/181630, 60/186,904,091419,351, and an application entitled “Protein Design Automation forProtein Libraries”, filed Feb. 12, 2001 (no U.S. serial number receivedyet) and PCT US98/07254, any combination of these scoring functions,either alone or in combination, may be used. Once the scoring functionsto be used are identified for each variable position, the preferredfirst step in the computational analysis comprises the determination ofthe interaction of each possible rotamer with all or part of theremainder of the protein. That is, the energy of interaction, asmeasured by one or more of the scoring functions, of each possiblerotamer at each variable residue position with either the backbone orother rotamers, is calculated. In a preferred embodiment, theinteraction of each rotamer with the entire remainder of the protein,i.e. both the entire template and all other rotamers, is done. However,as outlined above, it is possible to only model a portion of a protein,for example a domain of a larger protein, and thus in some cases, notall of the protein need be considered. The term “portion”, or similargrammatical equivalents thereof, as used herein, with regard to aprotein refers to a fragment of that protein. This fragment may range insize from 6–10 amino acid residues to the entire amino acid sequenceminus one amino acid. Accordingly, the term “portion”, as used herein,with regard to a nucleic refers to a fragment of that nucleic acid. Thisfragment may range in size from 10 nucleotides to the entire nucleicacid sequence minus one nucleotide.

In a preferred embodiment, the first step of the computationalprocessing is done by calculating two sets of interactions for eachrotamer at every position: the interaction of the rotamer side chainwith the template or backbone (the “singles” energy), and theinteraction of the rotamer side chain with all other possible rotamersat every other position (the “doubles” energy), whether that position isvaried or floated. It should be understood that the backbone in thiscase includes both the atoms of the protein structure backbone, as wellas the atoms of any fixed residues, wherein the fixed residues aredefined as a particular conformation of an amino acid.

Thus, “singles” (rotamer/template) energies are calculated for theinteraction of every possible rotamer at every variable residue positionwith the backbone, using some or all of the scoring functions. Thus, forthe hydrogen bonding scoring function, every hydrogen bonding atom ofthe rotamer and every hydrogen bonding atom of the backbone isevaluated, and the E_(HB) is calculated for each possible rotamer atevery variable position. Similarly, for the van der Waals scoringfunction, every atom of the rotamer is compared to every atom of thetemplate (generally excluding the backbone atoms of its own residue),and the E_(vdW) is calculated for each possible rotamer at everyvariable residue position. In addition, generally no van der Waalsenergy is calculated if the atoms are connected by three bonds or less.For the atomic salvation scoring function, the surface of the rotamer ismeasured against the surface of the template, and the E_(as) for eachpossible rotamer at every variable residue position is calculated. Thesecondary structure propensity scoring function is also considered as asingles energy, and thus the total singles energy may contain an E_(ss)term. As will be appreciated by those in the art, many of these energyterms will be close to zero, depending on the physical distance betweenthe rotamer and the template position; that is, the farther apart thetwo moieties, the lower the energy.

For the calculation of “doubles” energy (rotamer/rotamer), theinteraction energy of each possible rotamer is compared with everypossible rotamer at all other variable residue positions. Thus,“doubles” energies are calculated for the interaction of every possiblerotamer at every variable residue position with every possible rotamerat every other variable residue position, using some or all of thescoring functions. Thus, for the hydrogen bonding scoring function,every hydrogen bonding atom of the first rotamer and every hydrogenbonding atom of every possible second rotamer is evaluated, and theE_(HB) is calculated for each possible rotamer pair for any two variablepositions. Similarly, for the van der Waals scoring function, every atomof the first rotamer is compared to every atom of every possible secondrotamer, and the E_(vdW) is calculated for each possible rotamer pair atevery two variable residue positions. For the atomic salvation scoringfunction, the surface of the first rotamer is measured against thesurface of every possible second rotamer, and the E_(as) for eachpossible rotamer pair at every two variable residue positions iscalculated. The secondary structure propensity scoring function need notbe run as a “doubles” energy, as it is considered as a component of the“singles” energy. As will be appreciated by those in the art, many ofthese double energy terms will be close to zero, depending on thephysical distance between the first rotamer and the second rotamer; thatis, the farther apart the two moieties, the lower the energy.

In addition, as will be appreciated by those in the art, a variety offorce fields that can be used in the PDA calculations can be used,including, but not limited to, Dreiding I and Dreiding II [Mayo et al,J. Phys. Chem. 94:8897 (1990)], AMBER [Weiner et al., J. Amer. Chem.Soc. 106:765 (1984) and Weiner et al., J. Comp. Chem. 106:230 (1986)],MM2 [Allinger, J. Chem. Soc. 99:8127 (1977), Liljefors et al., J. Com.Chem. 8:1051 (1987)]; MMP2 [Sprague et al., J. Comp. Chem. 8:581(1987)); CHARMM [Brooks et al., J. Comp. Chem. 106:187 (1983)]; GROMOS;and MM3 [Allinger et al., J. Amer. Chem. Soc. 111:8551 (1989)], OPLS-AA[Jorgensen et al., J. Am. Chem. Soc. 118:11225–11236 (1996); Jorgensen,W.L.; BOSS, Version 4.1; Yale University: New Haven, Conn. (1999)]; OPLS[Jorgensen et al., J. Am. Chem. Soc.1 10:1657ff (1988); Jorgensen etal., J Am. Chem. Soc. 112:4768ff (1990)]; UNRES (United ResidueForcefield; Liwo et al., Protein Science 2:1697–1714 (1993); Liwo etat., Protein Science 2:1715–1731 (1993); Liwo et al., J. Comp. Chem.18:849–873 (1997); Liwo et al., J. Comp. Chem. 18:874–884 (1997); Liwoet al., J. Comp. Chem. 19:259–276 (1998); Forcefield for ProteinStructure Prediction (Liwo et al., Proc. Natl. Acad. Sci. U.S.A96:5482–5485 (1999)], ECEPP/3 [Liwo et al., J Protein Chem. 13(4):375–80(1994)]; A field (Weiner, et al., J. Am. Chem. Soc. 106:765–784); AMBER3.0 force field (U.C. Singh et al., Proc. Natl. Acad. Sci. U.S.A.82:755–759); CHARMM and CHARMM22 (Brooks et al., J. Comp. Chem.4:187–217); cvff3.0 [Dauber-Osguthorpe, et al., Proteins: Structure,Function and Genetics, 4:31–47 (1988)]; cff91 (Maple, et al., J. Comp.Chem. 15:162–182); also, the DISCOVER (cvff and cff91) and AMBERforcefields are used in the INSIGHT molecular modeling package(Biosym/MSI, San Diego Calif.) and HARMM is used in the QUANTA molecularmodeling package (Biosym/MSI, San Diego Calif.), all of which areexpressly incorporated by reference.

Once the singles and doubles energies are calculated and stored, thenext step of the computational processing may occur. As outlined in U.S.Ser. No. 09/127,926 and PCT US98107254, preferred embodiments utilize aDead End Elimination (DEE) step, and preferably a Monte Carlo step.

PDA, viewed broadly, has three components that may be varied to alterthe output (e.g. the primary library): the scoring functions used in theprocess; the filtering technique, and the sampling technique.

In a preferred embodiment, the scoring functions may be altered. In apreferred embodiment, the scoring functions outlined above may be biasedor weighted in a variety of ways. For example, a bias towards or awayfrom a reference sequence or family of sequences can be done; forexample, a bias towards wild-type or homolog residues may be used.Similarly, the entire protein or a fragment of it may be biased; forexample, the active site may be biased towards wild-type residues, ordomain residues towards a particular desired physical property can bedone. Furthermore, a bias towards or against increased energy can begenerated. Additional scoring function biases include, but are notlimited to applying electrostatic potential gradients or hydrophobicitygradients, adding a substrate or binding partner to the calculation, orbiasing towards a desired charge or hydrophobicity.

In addition, in an alternative embodiment, there are a variety ofadditional scoring functions that may be used. Additional scoringfunctions include, but are not limited to torsional potentials, orresidue pair potentials, or residue entropy potentials. Such additionalscoring functions can be used alone, or as functions for processing thelibrary after it is scored initially. For example, a variety offunctions derived from data on binding of peptides to MHC (MajorHistocompatibility Complex) can be used to rescore a library in order toeliminate proteins containing sequences which can potentially bind toMHC, i.e. potentially immunogenic sequences.

In a preferred embodiment, a variety of filtering techniques can bedone, including, but not limited to, DEE and its related counterparts.Additional filtering techniques include, but are not limited tobranch-and-bound techniques for finding optimal sequences (Gordon andMayo, Structure Fold. Des. 7:1089–98, 1999), and exhaustive enumerationof sequences.

As will be appreciated by those in the art, once an optimized sequenceor set of sequences is generated, a variety of sequence space samplingmethods can be done, either in addition to the preferred Monte Carlomethods, or instead of a Monte Carlo search. That is, once a sequence orset of sequences is generated, preferred methods utilize samplingtechniques to allow the generation of additional, related sequences fortesting.

These sampling methods can include the use of amino acid substitutions,insertions or deletions, or recombinations of one or more sequences. Asoutlined herein, a preferred embodiment utilizes a Monte Carlo search,which is a series of biased, systematic, or random jumps. However, thereare other sampling techniques that can be used, including Boltzmansampling, genetic algorithm techniques and simulated annealing. Inaddition, for all the sampling techniques, the kinds of jumps allowedcan be altered (e.g. random jumps to random residues, biased jumps (toor away from wild-type, for example), jumps to biased residues (to oraway from similar residues, for example, etc.). Jumps where multipleresidue positions are coupled (two residues always change together, ornever change together), jumps where whole sets of residues change toother sequences (e.g., recombination). Similarly, for all the samplingtechniques, the acceptance criteria of whether a sampling jump isaccepted can be altered.

In addition, it should be noted that the preferred methods of theinvention result in a rank ordered list of sequences; that is, thesequences are ranked on the basis of some objective criteria. However,as outlined herein, it is possible to create a set of non-orderedsequences, for example by generating a probability table directly (forexample using SCMF analysis or sequence alignment techniques) that listssequences without ranking them. The sampling techniques outlined hereincan be used in either situation.

In a preferred embodiment, Boltzman sampling is done. As will beappreciated by those in the art, the temperature criteria for Boltzmansampling can be altered to allow broad searches at high temperature andnarrow searches close to local optima at low temperatures (see e.g.,Metropolis et al., J. Chem. Phys. 21:1087, 1953).

In a preferred embodiment, the sampling technique utilizes geneticalgorithms, e.g., such as those described by Holland (Adaptation inNatural and Artificial Systems, 1975, Ann Arbor, U. Michigan Press).Genetic algorithm analysis generally takes generated sequences andrecombines them computationally, similar to a nucleic acid recombinationevent, in a manner similar to “gene shuffling”. Thus the “jumps” ofgenetic algorithm analysis generally are multiple position jumps. Inaddition, as outlined below, correlated multiple jumps may also be done.Such jumps can occur with different crossover positions and more thanone recombination at a time, and can involve recombination of two ormore sequences. Furthermore, deletions or insertions (random or biased)can be done. In addition, as outlined below, genetic algorithm analysismay also be used after the secondary library has been generated.

In a preferred embodiment, the sampling technique utilizes simulatedannealing, e.g., such as described by Kirkpatrick et al. [Science,220:671–680 (1983)]. Simulated annealing alters the cutoff for acceptinggood or bad jumps by altering the temperature. That is, the stringencyof the cutoff is altered by altering the temperature. This allows broadsearches at high temperature to new areas of sequence space, alteringwith narrow searches at low temperature to explore regions in detail.

In addition, as outlined below, these sampling methods can be used tofurther process a first set to generate additional sets of variant TNF-αproteins.

As used herein variant TNF-α proteins include TNF-α monomers.

The computational processing results in a set of optimized variant TNFprotein sequences. Optimized variant TNF-α protein sequences aregenerally different from the wild-type TNF-α sequence in structuralregions critical for receptor affinity, e.g. p55, p75. Preferably, eachoptimized variant TNF-α protein sequence comprises at least about 1variant amino acid from the starting or wild type sequence, with 3–5being preferred.

Thus, in the broadest sense, the present invention is directed tovariant TNF-α proteins that are antagonists of wild-type TNF-α. By“variant TNF-α proteins” herein is meant TNF-α proteins, which have beendesigned using the computational methods outlined herein to differ fromthe corresponding wild-type protein by at least 1 amino acid.

By “protein” herein is meant at least two covalently attached aminoacids, which includes proteins, polypeptides, oligopeptides andpeptides. The protein may be made up of naturally occurring amino acidsand peptide bonds, or synthetic peptidomimetic structures, i.e.,“analogs” such as peptoids [see Simon et al., Proc. Natl. Acd. Sci.U.S.A. 89(20:9367–71 (1992)], generally depending on the method ofsynthesis. Thus “amino acid”, or “peptide residue”, as used herein meansboth naturally occurring and synthetic amino acids. For example,homo-phenylalanine, citrulline, and noreleucine are considered aminoacids for the purposes of the invention. “Amino acid” also includesimino acid residues such as proline and hydroxyproline. In addition, anyamino acid representing a component of the variant TNF-α proteins can bereplaced by the same amino acid but of the opposite chirality. Thus, anyamino acid naturally occurring in the L-configuration (which may also bereferred to as the R or S, depending upon the structure of the chemicalentity) may be replaced with an amino acid of the same chemicalstructural type, but of the opposite chirality, generally referred to asthe D- amino acid but which can additionally be referred to as the R- orthe S-, depending upon its composition and chemical configuration. Suchderivatives have the property of greatly increased stability, andtherefore are advantageous in the formulation of compounds which mayhave longer in vivo half lives, when administered by oral, intravenous,intramuscular, intraperitoneal, topical, rectal, intraocular, or otherroutes. In the preferred embodiment, the amino acids are in the (S) orL-configuration. If non-naturally occurring side chains are used,non-amino acid substituents may be used, for example to prevent orretard in vivo degradations. Proteins including non-naturally occurringamino acids may be synthesized or in some cases, made recombinantly; seevan Hest et al., FEBS Lett 428(1–2) 68–70 May 22, 1998 and Tang et al.,Abstr. Pap Am. Chem. S218:U138-U138 Part 2 Aug. 22, 1999, both of whichare expressly incorporated by reference herein.

Aromatic amino acids may be replaced with D- or L-naphylalanine, D- orL-Phenylglycine, D- or L-2-thieneylalanine, D- or L-1-, 2-, 3- or4-pyreneylalanine, D- or L-3-thieneylalanine, D- orL-(2-pyridinyl)-alanine, D- or L-(3-pyridinyl)-alanine, D- orL-(2-pyrazinyl)-alanine, D- or L-(4-isopropyl)-phenylglycine,D-(trifluoromethyl)-phenylglycine, D-(trifluoromethyl)-phenylalanine,D-p-fluorophenylalanine, D- or L-p-biphenylphenylalanine, D- orL-p-methoxybiphenylphenylalanine, D- or L-2-indole(alkyl)alanines, andD- or L-alkylainines where alkyl may be substituted or unsubstitutedmethyl, ethyl, propyl, hexyl, butyl, pentyl, isopropyl, iso-butyl,sec-isotyl, iso-pentyl, non-acidic amino acids, of C1–C20.

Acidic amino acids can be substituted with non-carboxylate amino acidswhile maintaining a negative charge, and derivatives or analogs thereof,such as the non-limiting examples of (phosphono)alanine, glycine,leucine, isoleucine, threonine, or serine; or sulfated (e.g., —SO.sub.3H) threonine, serine, tyrosine.

Other substitutions may include unnatural hyroxylated amino acids maymade by combining “alkyl” with any natural amino acid. The term “alkyl”as used herein refers to a branched or unbranched saturated hydrocarbongroup of 1 to 24 carbon atoms, such as methyl, ethyl, n-propyl,isoptopyl, n-butyl, isobutyl, t-butyl, octyl, decyl, tetradecyl,hexadecyl, eicosyl, tetracisyl and the like. Alkyl includes heteroalkyl,with atoms of nitrogen, oxygen and sulfur. Preferred alkyl groups hereincontain 1 to 12 carbon atoms. Basic amino acids may be substituted withalkyl groups at any position of the naturally occurring amino acidslysine, arginine, ornithine, citrulline, or (guanidino)-acetic acid, orother (guanidino)alkyl-acetic acids, where “alkyl” is define as above.Nitrile derivatives (e.g., containing the CN-moiety in place of COOH)may also be substituted for asparagine or glutamine, and methioninesulfoxide may be substituted for methionine. Methods of preparation ofsuch peptide derivatives are well known to one skilled in the art.

In addition, any amide linkage in any of the variant TNF-α polypeptidescan be replaced by a ketomethylene moiety. Such derivatives are expectedto have the property of increased stability to degradation by enzymes,and therefore possess advantages for the formulation of compounds whichmay have increased in vivo half lives, as administered by oral,intravenous, intramuscular, intraperitoneal, topical, rectal,intraocular, or other routes.

Additional amino acid modifications of amino acids of variant TNF-αpolypeptides of to the present invention may include the following:Cysteinyl residues may be reacted with alpha-haloacetates (andcorresponding amines), such as 2-chloroacetic acid or chloroacetamide,to give carboxymethyl or carboxyamidomethyl derivatives. Cysteinylresidues may also be derivatized by reaction with compounds such asbromotrifluoroacetone, alpha-bromo-beta-(5-imidozoyl)propionic acid,chloroacetyl phosphate, N-alkylmaleimides, 3-nitro-2-pyridyl disulfide,methyl 2-pyridyl disulfide, p-chloromercuribenzoate,2-chloromercuri4-nitrophenol, or chloro-7-nitrobenzo-2-oxa-1,3-diazole.

Histidyl residues may be derivatized by reaction with compounds such asdiethylprocarbonate e.g., at pH 5.5–7.0 because this agent is relativelyspecific for the histidyl side chain, and para-bromophenacyl bromide mayalso be used; e.g., where the reaction is preferably performed in 0.1 Msodium cacodylate at pH 6.0.

Lysinyl and amino terminal residues may be reacted with compounds suchas succinic or other carboxylic acid anhydrides. Derivatization withthese agents is expected to have the effect of reversing the charge ofthe lysinyl residues. Other suitable reagents for derivatizingalpha-amino-containing residues include compounds such asimidoesters/e.g., as methyl picolinimidate; pyridoxal phosphate;pyridoxal; chloroborohydride; trinitrobenzenesulfonic acid;O-methylisourea; 2,4 pentanedione; and transaminase-catalyzed reactionwith glyoxylate.

Arginyl residues may be modified by reaction with one or severalconventional reagents, among them phenylglyoxal, 2,3-butanedione,1,2-cyciohexanedione, and ninhydrin according to known method steps.Derivatization of arginine residues requires that the reaction beperformed in alkaline conditions because of the high pKa of theguanidine functional group. Furthermore, these reagents may react withthe groups of lysine as well as the arginine epsilon-amino group.

The specific modification of tyrosyl residues per se is well-known, suchas for introducing spectral labels into tyrosyl residues by reactionwith aromatic diazonium compounds or tetranitromethane. N-acetylimidizoland tetranitromethane may be used to form O-acetyl tyrosyl species and3-nitro derivatives, respectively.

Carboxyl side groups (aspartyl or glutamyl) may be selectively modifiedby reaction with carbodiimides (R′—N—C—N—R′) such as1-cyclohexyl-3-(2-morpholinyl- (4-ethyl) carbodiimide or1-ethyl-3-carbodiimide. Furthermore aspartyl and glutamyl residues maybe converted to asparaginyl and glutaminyl residues by reaction withammonium ions.

Glutaminyl and asparaginyl residues may be frequently deamidated to thecorresponding glutamyl and aspartyl residues. Alternatively, theseresidues may be deamidated under mildly acidic conditions. Either formof these residues falls within the scope of the present invention.

The TNF-α proteins may be from any number of organisms, with TNF-αproteins from mammals being particularly preferred. Suitable mammalsinclude, but are not limited to, rodents (rats, mice, hamsters, guineapigs, etc.) primates, farm animals (including sheep, goats, pigs, cows,horses, etc) and in the most preferred embodiment, from humans (thesequence of which is depicted in FIG. 6; SEQ ID NO:2). As will beappreciated by those in the art, TNF-α proteins based on TNF-α proteinsfrom mammals other than humans may find use in animal models of humandisease.

The TNF proteins of the invention are antagonists of wild-type TNF-α. By“antagonists of wild-type TNF-α” herein is meant that the variant TNF-αprotein inhibits or significantly decreases the activation of receptorsignaling by wild-type TNF-α proteins. In a preferred embodiment, thevariant TNF-α protein interacts with the wild-type TNF-α protein suchthat the complex comprising the variant TNF-α and wild-type TNF-α isincapable of activating TNF receptors, i.e. p55 TNF-R or p75 TNF-R.

In a preferred embodiment, the variant TNF-α protein is a variant TNF-αprotein which functions as an antagonist of wild-type TNF-α. Preferably,the variant TNF-α protein preferentially interacts with wild-type TNF-αto form mixed trimers with the wild-type protein such that receptorbinding does not occur and/or TNF-α signaling is not initiated.

By mixed trimers herein is meant that monomers of wild-type and variantTNF-α proteins interact to form trimeric TNF-α. Mixed trimers maycomprise 1 variant TNF-α protein:2 wild-type TNF-α proteins, 2 variantTNF-α proteins:1 wild-type TNF-α protein. In some embodiments, trimersmay be formed comprising only variant TNF-α proteins.

The variant TNF-α antagonist proteins of the invention are highlyspecific for TNF-α antagonism relative to TNF-β antagonism. Additionalcharacteristics include improved stability, pharmacokinetics, and highaffinity for wild-type TNF-α. Variants with higher affinity towardwild-type TNF-α may be generated from variants exhibiting TNF-αantagonism as outlined above.

In a preferred embodiment, variant TNF-α proteins exhibit decreasedbiological activity as compared to wild-type TNF-α, including but notlimited to, decreased binding to the receptor, decreased activationand/or ultimately a loss of cytotoxic activity. By “cytotoxic activity”herein refers to the ability of wild-type TNF-α to selectively kill orinhibit cells. Variant TNF-α proteins that exhibit less than 50%biological activity are preferred. More preferred are variant TNF-αproteins that exhibit less than 25%, even more preferred are variantproteins that exhibit less than 15%, and most preferred are variantTNF-α proteins that exhibit less than 10% of a biological activity ofwild type TNF-α. Suitable assays include, but are not limited to, TNF acytotoxicity assays, DNA binding assays; transcription assays (usingreporter constructs; see Stavridi, supra); size exclusion chromatographyassays and radiolabeling/immuno-precipitation; see Corcoran et al.,supra); and stability assays (including the use of circular dichroism(CD) assays and equilibrium studies; see Mateu, supra); all of which areexpressly incorporated by reference.

In one embodiment, at least one property critical for binding affinityof the variant TNF-α proteins is altered when compared to the sameproperty of wild-type TNF-α and in particular, variant TNF-α proteinswith altered receptor affinity are preferred Particularly preferred arevariant TNF-α with altered affinity toward oligomerization to wild-typeTNF-α.

Thus, the invention provides variant TNF-α proteins with altered bindingaffinities such that the variant TNF-α proteins will preferentiallyoligomerize with wild-type TNF-α, but do not substantially interact withwild-type TNF receptors, i.e., p55, p75. “Preferentially” in this casemeans that given equal amounts of variant TNF-α monomers and wild-typeTNF-α monomers, at least 25% of the resulting trimers are mixed trimersof variant and wild-type TNF-α, with at least about 50% being preferred,and at least about 80–90% being particularly preferred. In other words,it is preferable that the variant TNF-α proteins of the invention havegreater affinity for wild-type TNF-α protein as compared to wild-typeTNF-α proteins. By “do not substantially interact with TNF receptors”herein is meant that the variant TNF-α proteins will not be able toassociate with either the p55 or p75 receptors to activate the receptorand initiate the TNF signaling pathway(s).

As outlined above, the invention provides variant TNF-α nucleic acidsencoding variant TNF-α polypeptides. The variant TNF-α polypeptidepreferably has at least one property, which is substantially differentfrom the same property of the corresponding naturally occurring TNFpolypeptide. The property of the variant TNF-α polypeptide is the resultthe PDA analysis of the present invention.

The term “altered property” or grammatical equivalents thereof in thecontext of a polypeptide, as used herein, further refers to anycharacteristic or attribute of a polypeptide that can be selected ordetected and compared to the corresponding property of a naturallyoccurring protein. These properties include, but are not limited tocytotoxic activity; oxidative stability, substrate specificity,substrate binding or catalytic activity, thermal stability, alkalinestability, pH activity profile, resistance to proteolytic degradation,kinetic association (K_(on)) and dissociation (K_(off)) rate, proteinfolding, inducing an immune response, ability to bind to a ligand,ability to bind to a receptor, ability to be secreted, ability to bedisplayed on the surface of a cell, ability to oligomerize, ability tosignal, ability to stimulate cell proliferation, ability to inhibit cellproliferation, ability to induce apoptosis, ability to be modified byphosphorylation or glycosylation, ability to treat disease.

Unless otherwise specified, a substantial change in any of theabove-listed properties, when comparing the property of a variant TNF-αpolypeptide to the property of a naturally occurring TNF protein ispreferably at least a 20%, more preferably, 50%, more preferably atleast a 2-fold increase or decrease.

A change in cytotoxic activity is evidenced by at least a 75% or greaterdecrease in cell death initiated by a variant TNF-α protein as comparedto wild-type protein.

A change in binding affinity is evidenced by at least a 5% or greaterincrease or decrease in binding affinity to wild-type TNF receptorproteins or to wild-type TNF-α.

A change in oxidative stability is evidenced by at least about 20%, morepreferably at least 50% increase of activity of a variant TNF-α proteinwhen exposed to various oxidizing conditions as compared to that ofwild-type TNF-α. Oxidative stability is measured by known procedures.

A change in alkaline stability is evidenced by at least about a 5% orgreater increase or decrease (preferably increase) in the half life ofthe activity of a variant TNF-α protein when exposed to increasing ordecreasing pH conditions as compared to that of wild-type TNF-α.Generally, alkaline stability is measured by known procedures.

A change in thermal stability is evidenced by at least about a 5% orgreater increase or decrease (preferably increase) in the half life ofthe activity of a variant TNF-α protein when exposed to a relativelyhigh temperature and neutral pH as compared to that of wild-type TNF-α.Generally, thermal stability is measured by known procedures.

Similarly, variant TNF-α proteins, for example are experimentally testedand validated in in vivo and in in vitro assays. Suitable assaysinclude, but are not limited to, e.g., examining the binding affinity ofvariant TNF-α proteins as compared to wild-type TNF-α proteins fornaturally occurring TNF-α and TNF receptor proteins such as p55 and p75,and can include quantitative comparisons comparing kinetic andequilibrium binding constants. The kinetic association rate (K_(on)) anddissociation rate (K_(off)), and the equilibrium binding constants(K_(d)) can be determined using surface plasmon resonance on a BlAcoreinstrument following the standard procedure in the literature [Pearce etal., Biochemistry 38:81–89 (1999)]. Again, as outlined herein, variantTNF-α proteins that preferentially form mixed trimers with wild-typeTNF-α proteins, but do not substantially interact with wild-typereceptor proteins are preferred.

In a preferred embodiment, the antigenic profile in the host animal ofthe variant TNF-α protein is similar, and preferably identical, to theantigenic profile of the host TNF-α; that is, the variant TNF-α proteindoes not significantly stimulate the host organism (e.g. the patient) toan immune response; that is, any immune response is not clinicallyrelevant and there is no allergic response or neutralization of theprotein by an antibody. That is, in a preferred embodiment, the variantTNF-α protein does not contain additional or different epitopes from theTNF-α. By “epitope” or “determinant” herein is meant a portion of aprotein which will generate and/or bind an antibody. Thus, in mostinstances, no significant amount of antibodies are generated to avariant TNF-α protein. In general, this is accomplished by notsignificantly altering surface residues, as outlined below nor by addingany amino acid residues on the surface which can become glycosylated, asnovel glycosylation can result in an immune response.

The variant TNF-α proteins and nucleic acids of the invention aredistinguishable from naturally occurring wild-type TNF-α. By “naturallyoccurring or wild type” or grammatical equivalents, herein is meant anamino acid sequence or a nucleotide sequence that is found in nature andincludes allelic variations; that is, an amino acid sequence or anucleotide sequence that usually has not been intentionally modified.Accordingly, by “non-naturally occurring” or “synthetic” or“recombinant” or grammatical equivalents thereof, herein is meant anamino acid sequence or a nucleotide sequence that is not found innature; that is, an amino acid sequence or a nucleotide sequence thatusually has been intentionally modified. It is understood that once arecombinant nucleic acid is made and reintroduced into a hostcell ororganism, it will replicate non-recombinantly, i.e., using the in vivocellular machinery ofthe host cell ratherthan in vitro manipulations,however, such nucleic acids, once produced recombinantly, althoughsubsequently replicated non-recombinantly, are still consideredrecombinant for the purpose of the invention. Representative amino acidand nucleotide sequences of a naturally occurring guman TNG-α are shownin FIG. (SEQ ID NOS:1–2) It should be noted that unless otherwisestated, all positional numbering of variant TNF-α proteins and variantTNF-α nucleic acids is based on these sequences. That is, as will beappreciated by those in the art, an alignment of TNF-α proteins andvariant TNF-α proteins can be done using standard programs, as isoutlined below, with the identification of “equivalent” positionsbetween the two proteins. Thus, the variant TNF-α proteins and nucleicacids of the invention are non-naturally occurring; that is, they do notexist in nature.

Thus, in a preferred embodiment, the variant TNF-α protein has an aminoacid sequence that differs from a wild-type TNF-α sequence by at least1–5% of the residues. That is, the variant TNF-α proteins of theinvention are less than about 97–99% identical to a wild-type TNF-αamino acid sequence. Accordingly, a protein is a “variant TNF-α protein”if the overall homology of the protein sequence to the amino acidsequence shown in FIG. 6 is preferably less than about 99%, morepreferably less than about 98%, even more preferably less than about 97%and mor preferably less than 95%. In some embodiments, the homology willbe as low as about 75–80%. Stated differently, based on the human TNFsequence of FIG. 6, variant TNF-α proteins have at least about 1 residuethat differs from the human TNF-α sequence, with at least about 2, 3, 4,or 5 different residues. Preferred variant TNF-α proteins have 3 to 5different residues.

Homology in this context means sequence similarity or identity, withidentity being preferred. As is known in the art, a number of differentprograms can be used to identify whether a protein (or nucleic acid asdiscussed below) has sequence identity or similarity to a knownsequence. Sequence identity and/or similarity is determined usingstandard techniques known in the art, including, but not limited to, thelocal sequence identity algorithm of Smith & Waterman, Adv. Appl. Math.,2:482 (1981), by the sequence identity alignment algorithm of Needleman& Wunsch, J. Mol. Biol., 48:443 (1970), by the search for similaritymethod of Pearson & Lipman, Proc. Natl. Acad. Sci. U.S.A., 85:2444(1988), by computerized implementations of these algorithms (GAP,BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package,Genetics Computer Group, 575 Science Drive, Madison, Wis.), the Best Fitsequence program described by Devereux et al., Nucl. Acid Res.,12:387–395 (1984), preferably using the default settings, or byinspection. Preferably, percent identity is calculated by FastDB basedupon the following parameters: mismatch penalty of 1; gap penalty of 1;gap size penalty of 0.33; and joining penalty of 30, “Current Methods inSequence Comparison and Analysis,” Macromolecule Sequencing andSynthesis, Selected Methods and Applications, pp 127–149 (1988), Alan R.Liss, Inc.

An example of a useful algorithm is PILEUP. PILEUP creates a multiplesequence alignment from a group of related sequences using progressive,pairwise alignments. It can also plot a tree showing the clusteringrelationships used to create the alignment. PILEUP uses a simplificationof the progressive alignment method of Feng & Doolittle, J. Mol. Evol.35:351–360 (1987); the method is similar to that described by Higgins &Sharp CABIOS 5:151–153 (1989). Useful PILEUP parameters including adefault gap weight of 3.00, a default gap length weight of 0.10, andweighted end gaps.

Another example of a useful algorithm is the BLAST algorithm, describedin: Altschul et al., J. Mol. Biol. 215, 403–410, (1990); Altschul etal., Nucleic Acids Res. 25:3389–3402 (1997); and Karlin et al., Proc.Natl. Acad. Sci. U.S.A 90:5873–5787 (1997). A particularly useful BLASTprogram is the WU-BLAST-2 program which was obtained from Altschul etal., Methods in Enzymology, 266:460–480 (1996). WU-BLAST-2 uses severalsearch parameters, most of which are set to the default values. Theadjustable parameters are set 11. The HSP S and HSP S2 parameters aredynamic values and are established by the program itself depending uponthe composition of the particular sequence and composition of theparticular database against which the sequence of interest is beingsearched; however, the values may be adjusted to increase sensitivity.

An additional useful algorithm is gapped BLAST as reported by Altschulet al., Nucl. Acids Res., 25:3389–3402. Gapped BLAST uses BLOSUM-62substitution scores; threshold T parameter set to 9; the two-hit methodto trigger ungapped extensions; charges gap lengths of k a cost of10+k₁X_(u) set to 16, and X_(g) set to 40 for database search stage andto 67 for the output stage of the algorithms. Gapped alignments aretriggered by a score corresponding to ˜22 bits.

A % amino acid sequence identity value is determined by the number ofmatching identical residues divided by the total number of residues ofthe “longer” sequence in the aligned region. The “longer” sequence isthe one having the most actual residues in the aligned region (gapsintroduced by WU-Blast-2 to maximize the alignment score are ignored).

In a similar manner, “percent (%) nucleic acid sequence identity” withrespect to the coding sequence of the polypeptides identified herein isdefined as the percentage of nucleotide residues in a candidate sequencethat are identical with the nucleotide residues in the coding sequenceof the cell cycle protein. A preferred method utilizes the BLASTN moduleof WU-BLAST-2 set to the default parameters, with overlap span andoverlap fraction set to 1 and 0.125, respectively.

The alignment may include the introduction of gaps in the sequences tobe aligned. In addition, for sequences which contain either more orfewer amino acids than the protein encoded by the sequence of FIG. 6, itis understood that in one embodiment, the percentage of sequenceidentity will be determined based on the number of identical amino acidsin relation to the total number of amino acids. Thus, for example,sequence identity of sequences shorter than that shown in FIG. 6, asdiscussed below, will be determined using the number of amino acids inthe shorter sequence, in one embodiment. In percent identitycalculations relative weight is not assigned to various manifestationsof sequence variation, such as, insertions, deletions, substitutions,etc.

In one embodiment, only identities are scored positively (+1) and allforms of sequence variation including gaps are assigned a value of “0”,which obviates the need for a weighted scale or parameters as describedbelow for sequence similarity calculations. Percent sequence identitycan be calculated, for example, by dividing the number of matchingidentical residues by the total number of residues of the “shorter”sequence in the aligned region and multiplying by 100. The “longer”sequence is the one having the most actual residues in the alignedregion.

Thus, the variant TNF-α proteins of the present invention may be shorteror longer than the amino acid sequence shown in FIG. 6B. Thus, in apreferred embodiment, included within the definition of variant TNFproteins are portions or fragments of the sequences depicted herein.Fragments of variant TNF-α proteins are considered variant TNF-αproteins if a0 they share at least one antigenic epitope; b) have atleast the indicated homology; c) and preferably have variant TNF-αbiological activity as defined herein.

In a preferred embodiment, as is more fully outlined below, the variantTNF-α proteins include further amino acid variations, as compared to awild type TNF-α, than those outlined herein. In addition, as outlinedherein, any of the variations depicted herein may be combined in any wayto form additional novel variant TNF-α proteins.

In addition, variant TNF-α proteins can be made that are longer thanthose depicted in the figures, for example, by the addition of epitopeor purification tags, as outlined herein, the addition of other fusionsequences, etc. For example, the variant TNF-α proteins of the inventionmay be fused to other therapeutic proteins or to other proteins such asFc or serum albumin for pharmacokinetic purposes. See for example U.S.Pat. Nos. 5,766,883 and 5,876,969, both of which are expresslyincorporated by reference.

In a preferred embodiment, the variant TNF-α proteins comprise residuesselected from the following positions 21, 30, 31, 32, 33, 35, 65, 66,67, 111, 112, 115, 140, 143, 144, 145, 146, and 147.

Also included within the invention are variant TNF-α proteins comprisingvariable residues in core, surface, and boundary residues.

Preferred amino acids for each position, including the human TNF-αresidues, are shown in FIG. 7 (SEQ ID NOS:23–44). Thus, for example, atposition 143, preferred amino acids are Glu, Asn, Gln, Ser, Arg, andLys; etc.

Preferred changes are as follows: D143E, D143N, D143S, A145R, A145K,A145E, E146K, E146R and A84V. These may be done either individually orin combination, with any combination being possible. However, asoutlined herein, preferred embodiments utilize at least 1 to 5, andpreferably more, positions in each variant TNF-α protein.

In a preferred embodiment, the variant TNF-α proteins of the inventionare human TNF-α conformers. By “conformer” herein is meant a proteinthat has a protein backbone 3D structure that is virtually the same buthas significant differences in the amino acid side chains. That is, thevariant TNF-α proteins of the invention define a conformer set, whereinall of the proteins of the set share a backbone structure and yet havesequences that differ by at least 1–3–5%. The three dimensional backbonestructure of a variant TNF-α protein thus substantially corresponds tothe three dimensional backbone structure of human TNF-α. “Backbone” inthis context means the non-side chain atoms: the nitrogen, carbonylcarbon and oxygen, and the a-carbon, and the hydrogens attached to thenitrogen and α-carbon. To be considered a conformer, a protein must havebackbone atoms that are no more than 2 Å from the human TNF-α structure,with no more than 1.5 Å being preferred, and no more than 1 Å beingparticularly preferred. In general, these distances may be determined intwo ways. In one embodiment, each potential conformer is crystallizedand its three dimensional structure determined. Alternatively, as theformer is quite tedious, the sequence of each potential conformer is runin the PDA program to determine whether it is a conformer.

Variant TNF-α proteins may also be identified as being encoded byvariant TNF-α nucleic acids. In the case of the nucleic acid, theoverall homology of the nucleic acid sequence is commensurate with aminoacid homology but takes into account the degeneracy in the genetic codeand codon bias of different organisms. Accordingly, the nucleic acidsequence homology may be either lower or higher than that of the proteinsequence, with lower homology being preferred.

In a preferred embodiment, a variant TNF-α nucleic acid encodes avariant TNF-α protein. As will be appreciated by those in the art, dueto the degeneracy of the genetic code, an extremely large number ofnucleic acids may be made, all of which encode the variant TNF-αproteins of the present invention. Thus, having identified a particularamino acid sequence, those skilled in the art could make any number ofdifferent nucleic acids, by simply modifying the sequence of one or morecodons in a way which does not change the amino acid sequence of thevariant TNF-α.

In one embodiment, the nucleic acid homology is determined throughhybridization studies. Thus, for example, nucleic acids which hybridizeunder high stringency to the nucleic acid sequence shown in FIG. 6A orits complement and encode a variant TNF-α protein is considered avariant TNF-α gene.

High stringency conditions are known in the art; see for exampleManiatis et al., Molecular Cloning: A Laboratory Manual, 2d Edition,1989, and Short Protocols in Molecular Biology, ed. Ausubel, et al.,both of which are hereby incorporated by reference. Stringent conditionsare sequence-dependent and will be different in different circumstances.Longer sequences hybridize specifically at higher temperatures. Anextensive guide to the hybridization of nucleic acids is found inTijssen, Techniques in Biochemistry and Molecular Biology—Hybridizationwith Nucleic Acid Probes, “Overview of principles of hybridization andthe strategy of nucleic acid assays” (1993). Generally, stringentconditions are selected to be about 5–10° C. lower than the thermalmelting point (T_(m)) for the specific sequence at a defined ionicstrength and pH. The T_(m) is the temperature (under defined ionicstrength, pH and nucleic acid concentration) at which 50% of the probescomplementary to the target hybridize to the target sequence atequilibrium (as the target sequences are present in excess, at T_(m),50% of the probes are occupied at equilibrium). Stringent conditionswill be those in which the salt concentration is less than about 1.0 Msodium ion, typically about 0.01 to 1.0 M sodium ion concentration (orother salts) at pH 7.0 to 8.3 and the temperature is at least about 30°C. for short probes (e.g. 10 to 50 nucleotides) and at least about 60°C. for long probes (e.g. greater than 50 nucleotides). Stringentconditions may also be achieved with the addition of destabilizingagents such as formamide.

In another embodiment, less stringent hybridization conditions are used;for example, moderate or low stringency conditions may be used, as areknown in the art; see Maniatis and Ausubel, supra, and Tijssen, supra.

The variant TNF-α proteins and nucleic acids of the present inventionare recombinant. As used herein, “nucleic acid” may refer to either DNAor RNA, or molecules which contain both deoxy- and ribonucleotides. Thenucleic acids include genomic DNA, cDNA and oligonucleotides includingsense and anti-sense nucleic acids. Such nucleic acids may also containmodifications in the ribose-phosphate backbone to increase stability andhalf life of such molecules in physiological environments.

The nucleic acid may be double stranded, single stranded, or containportions of both double stranded or single stranded sequence. As will beappreciated by those in the art, the depiction of a single strand(“Watson”) also defines the sequence of the other strand (“Crick”); thusthe sequence depicted in FIG. 6 also includes the complement of thesequence. By the term “recombinant nucleic acid” herein is meant nucleicacid, originally formed in vitro, in general, by the manipulation ofnucleic acid by endonucleases, in a form not normally found in nature.Thus an isolated variant TNF-α nucleic acid, in a linear form, or anexpression vector formed in vitro by ligating DNA molecules that are notnormally joined, are both considered recombinant for the purposes ofthis invention. It is understood that once a recombinant nucleic acid ismade and reintroduced into a host cell or organism, it will replicatenon-recombinantly, i.e. using the in vivo cellular machinery of the hostcell rather than in vitro manipulations; however, such nucleic acids,once produced recombinantly, although subsequently replicatednon-recombinantly, are still considered recombinant for the purposes ofthe invention.

Similarly, a “recombinant protein” is a protein made using recombinanttechniques, i.e. through the expression of a recombinant nucleic acid asdepicted above. A recombinant protein is distinguished from naturallyoccurring protein by at least one or more characteristics. For example,the protein may be isolated or purified away from some or all of theproteins and compounds with which it is normally associated in its wildtype host, and thus may be substantially pure. For example, an isolatedprotein is unaccompanied by at least some of the material with which itis normally associated in its natural state, preferably constituting atleast about 0.5%, more preferably at least about 5% by weight of thetotal protein in a given sample. A substantially pure protein comprisesat least about 75% by weight of the total protein, with at least about80% being preferred, and at least about 90% being particularlypreferred. The definition includes the production of a variant TNF-αprotein from one organism in a different organism or host cell.Alternatively, the protein may be made at a significantly higherconcentration than is normally seen, through the use of a induciblepromoter or high expression promoter, such that the protein is made atincreased concentration levels. Furthermore, all of the variant TNF-αproteins outlined herein are in a form not normally found in nature, asthey contain amino acid substitutions, insertions and deletions, withsubstitutions being preferred, as discussed below.

Also included within the definition of variant TNF-α proteins of thepresent invention are amino acid sequence variants of the variant TNF-αsequences outlined herein and shown in the Figures. That is, the variantTNF-α proteins may contain additional variable positions as compared tohuman TNF-α. These variants fall into one or more of three classes:substitutional, insertional or deletional variants. These variantsordinarily are prepared by site specific mutagenesis of nucleotides inthe DNA encoding a variant TNF-α protein, using cassette or PCRmutagenesis or other techniques well known in the art, to produce DNAencoding the variant, and thereafter expressing the DNA in recombinantcell culture as outlined above. However, variant TNF-α protein fragmentshaving up to about 100–150 residues may be prepared by in vitrosynthesis using established techniques. Amino acid sequence variants arecharacterized by the predetermined nature of the variation, a featurethat sets them apart from naturally occurring allelic or interspeciesvariation of the variant TNF-α protein amino acid sequence. The variantstypically exhibit the same qualitative biological activity as thenaturally occurring analogue, although variants can also be selectedwhich have modified characteristics as will be more fully outlinedbelow.

While the site or region for introducing an amino acid sequencevariation is predetermined, the mutation per se need not bepredetermined. For example, in order to optimize the performance of amutation at a given site, random mutagenesis may be conducted at thetarget codon or region and the expressed variant TNF-α proteins screenedfor the optimal combination of desired activity. Techniques for makingsubstitution mutations at predetermined sites in DNA having a knownsequence are well known, for example, M13 primer mutagenesis and PCRmutagenesis. Screening of the mutants is done using assays of variantTNF-α protein activities.

Amino acid substitutions are typically of single residues; insertionsusually will be on the order of from about 1 to 20 amino acids, althoughconsiderably larger insertions may be tolerated. Deletions range fromabout 1 to about 20 residues, although in some cases deletions may bemuch larger.

Substitutions, deletions, insertions or any combination thereof may beused to arrive at a final derivative. Generally these changes are doneon a few amino acids to minimize the alteration of the molecule.However, larger changes may be tolerated in certain circumstances. Whensmall alterations in the characteristics of the variant TNF-α proteinare desired, substitutions are generally made in accordance with thefollowing chart:

Chart I Original Residue Exemplary Substitutions Ala Ser Arg Lys AsnGln, His Asp Glu Cys Ser, Ala Gln Asn Glu Asp Gly Pro His Asn, Gln IleLeu, Val Leu Ile, Val Lys Arg, Gln, Glu Met Leu, Ile Phe Met, Leu, TyrSer Thr Thr Ser Trp Tyr Tyr Trp, Phe Val Ile, Leu

Substantial changes in function or immunological identity are made byselecting substitutions that are less conservative than those shown inChart 1. For example, substitutions may be made which more significantlyaffect: the structure of the polypeptide backbone in the area of thealteration, for example the alpha-helical or beta-sheet structure; thecharge or hydrophobicity of the molecule at the target site; or the bulkof the side chain. The substitutions which in general are expected toproduce the greatest changes in the polypeptide's properties are thosein which (a) a hydrophilic residue, e.g. seryf or threonyl, issubstituted for (or by) a hydrophobic residue, e.g. leucyl, isoleucyl,phenylalanyl, valyl or alanyl; (b) a cysteine or proline is substitutedfor (or by) any other residue; (c) a residue having an electropositiveside chain, e.g. lysyl, arginyl, or histidyl, is substituted for (or by)an electronegative residue, e.g. glutamyl or aspartyl; or (d) a residuehaving a bulky side chain, e.g. phenylalanine, is substituted for (orby) one not having a side chain, e.g. glycine.

The variants typically exhibit the same qualitative biological activityand will elicit the same immune response as the original variant TNF-αprotein, although variants also are selected to modify thecharacteristics of the variant TNF-α proteins as needed. Alternatively,the variant may be designed such that the biological activity of thevariant TNF-α protein is altered. For example, glycosylation sites maybe altered or removed. Similarly, the biological function may bealtered; for example, in some instances it may be desirable to have moreor less potent TNF-α activity.

In a preferred embodiment, also included within the invention aresoluble p55 variant TNF proteins and nucleic acids. In this embodiment,the soluble p55 variant TNF can serve as an antagonist to receptorsignaling. By “serving as an antagonist to receptor signaling” herein ismeant that the soluble p55 variant TNF proteins preferentially interactwith wild-type TNF-α to block or significantly decrease TNF-α receptoractivated signaling.

Thus, the computational processing results described above may be usedto generate a set of optimized variant p55 protein sequences. Optimizedvariant p55 protein sequences are generally different from wild-type p55sequences in at least about 1 variant amino acid.

In a preferred embodiment variant TNF p55 proteins are fused to a humanTNF receptor-associated factor (TRAF) trimerization domain. In apreferred embodiment, the C termini of optimized variant TNF p55receptors will be fused to TRAF trimerization domains (i.e., leucinezipper motif).

Fusion of trimerization domains from TRAF proteins to TNFR molecules caninduce trimerization, resulting in higher avidity for TNFa therebycreating a more potent TNFa inhibitor than the monomeric soluble TNFR.These trimerization domains can be used to induce the trimerization ofany protein where this may be desirable, including TNFalpha, TNFbeta,TNF receptor (p55 and p75), and other members of the TNF receptor familyincluding NGF receptor, CD27, CD30, CD40, fas antigen. Other peptidesthat are known to form trimeric coiled coils could also be used,including pil (Harbury, Kim and Alber, 1994).

While the description herein is focused on TNF-α variants, as will beappreciated by those in the art, the embodiments and definitions can beapplied to soluble p55 variant TNF proteins.

The variant TNF-α proteins and nucleic acids of the invention can bemade in a number of ways. Individual nucleic acids and proteins can bemade as known in the art and outlined below. Alternatively, libraries ofvariant TNF-α proteins can be made for testing.

In a preferred embodiment, sets or libraries of variant TNF-α proteinsare generated from a probability distribution table. As outlined herein,there are a variety of methods of generating a probability distributiontable, including using PDA, sequence alignments, forcefield calculationssuch as SCMF calculations, etc. In addition, the probabilitydistribution can be used to generate information entropy scores for eachposition, as a measure of the mutational frequency observed in thelibrary.

In this embodiment, the frequency of each amino acid residue at eachvariable position in the list is identified. Frequencies can bethresholded, wherein any variant frequency lower than a cutoff is set tozero. This cutoff is preferably 1%, 2%, 5%, 10% or 20%, with 10% beingparticularly preferred. These frequencies are then built into thevariant TNF-α library. That is, as above, these variable positions arecollected and all possible combinations are generated, but the aminoacid residues that “fill” the library are utilized on a frequency basis.Thus, in a non-frequency based library, a variable position that has 5possible residues will have 20% of the proteins comprising that variableposition with the first possible residue, 20% with the second, etc.However, in a frequency based library, a variable position that has 5possible residues with frequencies of 10%, 15%, 25%, 30% and 20%,respectively, will have 10% of the proteins comprising that variableposition with the first possible residue, 15% of the proteins with thesecond residue, 25% with the third, etc. As will be appreciated by thosein the art, the actual frequency may depend on the method used toactually generate the proteins; for example, exact frequencies may bepossible when the proteins are synthesized. However, when thefrequency-based primer system outlined below is used, the actualfrequencies at each position will vary, as outlined below.

As will be appreciated by those in the art and outlined herein,probability distribution tables can be generated in a variety of ways.In addition to the methods outlined herein, self-consistent mean field(SCMF) methods can be used in the direct generation of probabilitytables. SCMF is a deterministic computational method that uses a meanfield description of rotamer interactions to calculate energies.

A probability table generated in this way can be used to createlibraries as described herein. SCMF can be used in three ways: thefrequencies of amino acids and rotamers for each amino acid are listedat each position; the probabilities are determined directly from SCMF(see Delarue et la. Pac. Symp. Biocomput. 109–21 (1997), expresslyincorporated by reference). In addition, highly variable positions andnon-variable positions can be identified. Alternatively, another methodis used to determine what sequence is jumped to during a search ofsequence space; SCMF is used to obtain an accurate energy for thatsequence; this energy is then used to rank it and create a rank-orderedlist of sequences (similar to a Monte Carlo sequence list). Aprobability table showing the frequencies of amino acids at eachposition can then be calculated from this list (Koehl et al., J. Mol.Biol. 239–249 (1994); Koehl et al., Nat. Struc. Biol. 2:163 (1995);Koehl et al., Curr. Opin. Struct. Biol. 6:222 (1996); Koehl et al., J.Mol. Bio. 293:1183 (1999); Koehl et al., J. Mol. Biol. 293:1161 (1999);Lee J. Mol. Biol. 236:918 (1994); and Vasquez Biopolymers 36:53–70(1995); all of which are expressly incorporated by reference. Similarmethods include, but are not limited to, OPLS-AA (Jorgensen, et al., J.Am. Chem. Soc. (1996), v 118, pp 11225–11236; Jorgensen, W. L.; BOSS,Version 4.1; Yale University: New Haven, Conn. (1999)); OPLS (Jorgensen,et al., J. Am. Chem. Soc. (1988), v 110, pp 1657ff; Jorgensen, et al., JAm. Chem. Soc. (1990), v 112, pp 4768ff); UNRES (United ResidueForcefield; Liwo, et al., Protein Science (1993), v 2, pp1697–1714;Liwo, et al., Protein Science (1993), v 2, pp1715–1731; Liwo, et al., J.Comp. Chem. (1997), v 18, pp849–873; Liwo, et al., J. Comp. Chem.(1997), v 18, pp874–884; Liwo, et al., J. Comp. Chem. (1998), v 19,pp259–276; Forcefield for Protein Structure Prediction (Liwo, et al.,Proc. Natl. Acad. Sci. USA (1999), v 96, pp5482–5485); ECEPP/3 (Liwo etal., J Protein Chem 1994 May;13(4):375–80); AMBER 1.1 force field(Weiner, et al., J. Am. Chem. Soc. v106, pp765–784); AMBER 3.0 forcefield (U.C. Singh et al., Proc. Natl. Acad. Sci. USA (82:755–759);CHARMM and CHARMM22 (Brooks, et al., J. Comp. Chem. v4, pp 187–217);cvff3.0 (Dauber-Osguthorpe, et aI.,(1988) Proteins: Structure, Functionand Genetics, v4,pp3147); cff9l (Maple, etal., J. Comp. Chem. v15,162–182); also, the DISCOVER (cvff and cff9l) and AMBER forcefields areused in the INSIGHT molecular modeling package (Biosym/MSI, San DiegoCalif.) and HARMM is used in the QUANTA molecular modeling package(Biosym/MSI, San Diego Calif.).

In addition, as outlined herein, a preferred method of generating aprobability distribution table is through the use of sequence alignmentprograms. In addition, the probability table can be obtained by acombination of sequence alignments and computational approaches. Forexample, one can add amino acids found in the alignment of homologoussequences to the result of the computation. Preferable one can add thewild type amino acid identity to the probability table if it is notfound in the computation.

As will be appreciated, a variant TNF-α library created by recombiningvariable positions and/or residues at the variable position may not bein a rank-ordered list. In some embodiments, the entire list may just bemade and tested. Alternatively, in a preferred embodiment, the variantTNF-α library is also in the form of a rank ordered list. This may bedone for several reasons, including the size of the library is still toobig to generate experimentally, or for predictive purposes. This may bedone in several ways. In one embodiment, the library is ranked using thescoring functions of PDA to rank the library members. Alternatively,statistical methods could be used. For example, the library may beranked by frequency score; that is, proteins containing the most of highfrequency residues could be ranked higher, etc. This may be done byadding or multiplying the frequency at each variable position togenerate a numerical score. Similarly, the library different positionscould be weighted and then the proteins scored; for example, thosecontaining certain residues could be arbitrarily ranked.

In a preferred embodiment, the different protein members of the variantTNF-α library may be chemically synthesized. This is particularly usefulwhen the designed proteins are short, preferably less than 150 aminoacids in length, with less than 100 amino acids being preferred, andless than 50 amino acids being particularly preferred, although as isknown in the art, longer proteins can be made chemically orenzymatically. See for example Wilken et al, Curr. Opin. Biotechnol.9:412–26 (1998), hereby expressly incorporated by reference.

In a preferred embodiment, particularly for longer proteins or proteinsfor which large samples are desired, the library sequences are used tocreate nucleic acids such as DNA which encode the member sequences andwhich can then be cloned into host cells, expressed and assayed, ifdesired. Thus, nucleic acids, and particularly DNA, can be made whichencodes each member protein sequence. This is done using well knownprocedures. The choice of codons, suitable expression vectors andsuitable host cells will vary depending on a number of factors, and canbe easily optimized as needed.

In a preferred embodiment, multiple PCR reactions with pooledoligonucleotides is done, as is generally depicted in the Figures. Inthis embodiment, overlapping oligonucleotides are synthesized whichcorrespond to the full length gene. Again, these oligonucleotides mayrepresent all of the different amino acids at each variant position orsubsets.

In a preferred embodiment, these oligonucleotides are pooled in equalproportions and multiple PCR reactions are performed to create fulllength sequences containing the combinations of mutations defined by thelibrary. In addition, this may be done using error-prone PCR methods.

In a preferred embodiment, the different oligonucleotides are added inrelative amounts corresponding to the probability distribution table.The multiple PCR reactions thus result in full length sequences with thedesired combinations of mutations in the desired proportions.

The total number of oligonucleotides needed is a function of the numberof positions being mutated and the number of mutations being consideredat these positions: (number of oligos for constant positions)+M1+M2+M3+. . . Mn=(total number of oligos required), where Mn is the number ofmutations considered at position n in the sequence.

In a preferred embodiment, each overlapping oligonucleotide comprisesonly one position to be varied; in alternate embodiments, the variantpositions are too close together to allow this and multiple variants peroligonucleotide are used to allow complete recombination of all thepossibilities. That is, each oligo can contain the codon for a singleposition being mutated, or for more than one position being mutated. Themultiple positions being mutated must be close in sequence to preventthe oligo length from being impractical. For multiple mutating positionson an oligonucleotide, particular combinations of mutations can beincluded or excluded in the library by including or excluding theoligonucleotide encoding that combination. For example, as discussedherein, there may be correlations between variable regions; that is,when position X is a certain residue, position Y must (or must not) be aparticular residue. These sets of variable positions are sometimesreferred to herein as a “cluster”. When the clusters are comprised ofresidues close together, and thus can reside on one oligonucleotideprimer, the clusters can be set to the “good” correlations, andeliminate the bad combinations that may decrease the effectiveness ofthe library. However, if the residues of the cluster are far apart insequence, and thus will reside on different oligonucleotides forsynthesis, it may be desirable to either set the residues to the “good”correlation, or eliminate them as variable residues entirely. In analternative embodiment, the library may be generated in several steps,so that the cluster mutations only appear together. This procedure, i.e.the procedure of identifying mutation clusters and either placing themon the same oligonucleotides or eliminating them from the library orlibrary generation in several steps preserving clusters, canconsiderably enrich the experimental library with properly foldedprotein. Identification of clusters can be carried out by a number ofways, e.g. by using known pattern recognition methods, comparisons offrequencies of occurrence of mutations or by using energy analysis ofthe sequences to be experimentally generated (for example, if the energyof interaction is high, the positions are correlated). Thesecorrelations may be positional correlations (e.g. variable positions 1and 2 always change together or never change together) or sequencecorrelations (e.g. if there is residue A at position 1, there is alwaysresidue B at position 2). See: Pattern discovery in Biomolecular Data:Tools, Techniques, and Applications; edited by Jason T. L. Wang, BruceA. Shapiro, Dennis Shasha. New York: Oxford University, 1999; Andrews,Harry C. Introduction to mathematical techniques in pattern recognition;New York, Wiley-lnterscience [1972];

Applications of Pattern Recognition; Editor, K. S. Fu. Boca Raton, Fla.CRC Press, 1982; Genetic Algorithms for Pattern Recognition; edited bySankar K. Pal, Paul P. Wang. Boca Raton: CRC Press, c1996; Pandya,Abhijit S., Pattern recognition with neural networks in C++/Abhijit S.Pandya, Robert B. Macy. Boca Raton, Fla.: CRC Press, 1996; Handbook ofpattern recognition & computer vision I edited by C. H. Chen, L. F. Pau,P. S. P. Wang. 2nd ed. Singapore; River Edge, N.J.: World Scientific,c1999; Friedman, Introduction to Pattern Recognition: Statistical,Structural, Neural, and Fuzy Logic Approaches; River Edge, N.J.: WorldScientific, c1999, Series title: Series in machine perception andartificial intelligence; vol. 32; all of which are expresslyincorporated by reference. In addition, programs used to search forconsensus motifs can be used as well.

In addition, correlations and shuffling can be fixed or optimized byaltering the design of the oligonucleotides; that is, by deciding wherethe oligonucleotides (primers) start and stop (e.g. where the sequencesare “cut”). The start and stop sites of oligos can be set to maximizethe number of clusters that appear in single oligonucleotides, therebyenriching the library with higher scoring sequences. Differentoligonucleotide start and stop site options can be computationallymodeled and ranked according to number of clusters that are representedon single oligos, or the percentage of the resulting sequencesconsistent with the predicted library of sequences.

The total number of oligonucleotides required increases when multiplemutable positions are encoded by a single oligonucleotide. The annealedregions are the ones that remain constant, i.e. have the sequence of thereference sequence.

Oligonucleotides with insertions or deletions of codons can be used tocreate a library expressing different length proteins. In particularcomputational sequence screening for insertions or deletions can resultin secondary libraries defining different length proteins, which can beexpressed by a library of pooled oligonucleotide of different lengths.

In a preferred embodiment, the variant TNF-α library is done byshuffling the family (e.g. a set of variants); that is, some set of thetop sequences (if a rank-ordered list is used) can be shuffled, eitherwith or without error-prone PCR. “Shuffling” in this context means arecombination of related sequences, generally in a random way. It caninclude “shuffling” as defined and exemplified in U.S. Pat. Nos.5,830,721; 5,811,238; 5,605,793; 5,837,458 and PCT US/19256, all ofwhich are expressly incorporated by reference in their entirety. Thisset of sequences can also be an artificial set; for example, from aprobability table (for example generated using SCMF) or a Monte Carloset. Similarly, the “family” can be the top 10 and the bottom 10sequences, the top 100 sequence, etc. This may also be done usingerror-prone PCR.

Thus, in a preferred embodiment, in silico shuffling is done using thecomputational methods described herein. That is, starting with eithertwo libraries or two sequences, random recombinations of the sequencescan be generated and evaluated.

In a preferred embodiment, error-prone PCR is done to generate thevariant TNF-α library. See U.S. Pat. Nos. 5,605,793, 5,811,238, and5,830,721, all of which are hereby incorporated by reference. This canbe done on the optimal sequence or on top members of the library, orsome other artificial set or family. In this embodiment, the gene forthe optimal sequence found in the computational screen of the primarylibrary can be synthesized. Error prone PCR is then performed on theoptimal sequence gene in the presence of oligonucleotides that code forthe mutations at the variant positions of the library (biasoligonucleotides). The addition of the oligonucleotides will create abias favoring the incorporation of the mutations in the library.Alternatively, only oligonucleotides for certain mutations may be usedto bias the library.

In a preferred embodiment, gene shuffling with error prone PCR can beperformed on the gene for the optimal sequence, in the presence of biasoligonucleotides, to create a DNA sequence library that reflects theproportion of the mutations found in the variant TNF-α library. Thechoice of the bias oligonucleotides can be done in a variety of ways;they can chosen on the basis of their frequency, i.e. oligonucleotidesencoding high mutational frequency positions can be used; alternatively,oligonucleotides containing the most variable positions can be used,such that the diversity is increased; if the secondary library isranked, some number of top scoring positions can be used to generatebias oligonucleotides; random positions may be chosen; a few top scoringand a few low scoring ones may be chosen; etc. What is important is togenerate new sequences based on preferred variable positions andsequences.

In a preferred embodiment, PCR using a wlid type gene or other gene canbe used, as is schematically depicted in the Figures. In thisembodiment, a starting gene is used; generally, although this is notrequired, the gene is usually the wild type gene. In some cases it maybe the gene encoding the global optimized sequence, or any othersequence of the list, or a consensus sequence obtained e.g. fromaligning homologous sequences from different organisms. In thisembodiment, oligonucleotides are used that correspond to the variantpositions and contain the different amino acids of the library. PCR isdone using PCR primers at the termini, as is known in the art. Thisprovides two benefits; the first is that this generally requires feweroligonucleotides and can result in fewer errors. In addition, it hasexperimental advantages in that if the wild type gene is used, it neednot be synthesized.

In addition, there are several other techniques that can be used, asexemplified in the figures. In a preferred embodiment, ligation of PCRproducts is done.

In a preferred embodiment, a variety of additional steps may be done tothe variant TNF-α library; for example, further computational processingcan occur, different variant TNF-α libraries can be recombined, orcutoffs from different libraries can be combined. In a preferredembodiment, a variant TNF-α library may be computationally remanipulatedto form an additional variant TNF-α library (sometimes referred toherein as “tertiary libraries”). For example, any of the variant TNF-αlibrary sequences may be chosen for a second round of PDA, by freezingor fixing some or all of the changed positions in the first library.Alternatively, only changes seen in the last probability distributiontable are allowed. Alternatively, the stringency of the probabilitytable may be altered, either by increasing or decreasing the cutoff forinclusion. Similarly, the variant TNF-α library may be recombinedexperimentally after the first round; for example, the best gene/genesfrom the first screen may be taken and gene assembly redone (usingtechniques outlined below, multiple PCR, error prone PCR, shuffling,etc.). Alternatively, the fragments from one or more good gene(s) tochange probabilities at some positions. This biases the search to anarea of sequence space found in the first round of computational andexperimental screening.

In a preferred embodiment, a tertiary library can be generated fromcombining different variant TNF-α libraries. For example, a probabilitydistribution table from a first variant TNF-α library can be generatedand recombined, either computationally or experimentally, as outlinedherein. A PDA variant TNF-α library may be combined with a sequencealignment variant TNF-α library, and either recombined (again,computationally or experimentally) or just the cutoffs from each joinedto make a new tertiary library. The top sequences from several librariescan be recombined. Sequences from the top of a library can be combinedwith sequences from the bottom of the library to more broadly samplesequence space, or only sequences distant from the top of the librarycan be combined. Variant TNF-α libraries that analyzed different partsof a protein can be combined to a tertiary library that treats thecombined parts of the protein.

In a preferred embodiment, a tertiary library can be generated usingcorrelations in a variant TNF-α library. That is, a residue at a firstvariable position may be correlated to a residue at second variableposition (or correlated to residues at additional positions as well).For example, two variable positions may sterically or electrostaticallyinteract, such that if the first residue is X, the second residue mustbe Y. This may be either a positive or negative correlation.

Using the nucleic acids of the present invention which encode a variantTNF-α protein, a variety of expression vectors are made. The expressionvectors may be either self-replicating extrachromosomal vectors orvectors which integrate into a host genome. Generally, these expressionvectors include transcriptional and translational regulatory nucleicacid operably linked to the nucleic acid encoding the variant TNF-αprotein. The term “control sequences” refers to DNA sequences necessaryfor the expression of an operably linked coding sequence in a particularhost organism. The control sequences that are suitable for prokaryotes,for example, include a promoter, optionally an operator sequence, and aribosome binding site. Eukaryotic cells are known to utilize promoters,polyadenylation signals, and enhancers.

Nucleic acid is “operably linked” when it is placed into a functionalrelationship with another nucleic acid sequence. For example, DNA for apresequence or secretory leader is operably linked to DNA for apolypeptide if it is expressed as a preprotein that participates in thesecretion of the polypeptide; a promoter or enhancer is operably linkedto a coding sequence if it affects the transcription of the sequence; ora ribosome binding site is operably linked to a coding sequence if it ispositioned so as to facilitate translation.

In a preferred embodiment, when the endogenous secretory sequence leadsto a low level of secretion of the naturally occurring protein or of thevariant TNF-α protein, a replacement of the naturally occurringsecretory leader sequence is desired. In this embodiment, an unrelatedsecretory leader sequence is operably linked to a variant TNF-α encodingnucleic acid leading to increased protein secretion. Thus, any secretoryleader sequence resulting in enhanced secretion of the variant TNF-αprotein, when compared to the secretion of TNF-α and its secretorysequence, is desired. Suitable secretory leader sequences that lead tothe secretion of a protein are know in the art.

In another preferred embodiment, a secretory leader sequence of anaturally occurring protein or a protein is removed by techniques knownin the art and subsequent expression results in intracellularaccumulation of the recombinant protein.

Generally, “operably linked” means that the DNA sequences being linkedare contiguous, and, in the case of a secretory leader, contiguous andin reading phase. However, enhancers do not have to be contiguous.Linking is accomplished by ligation at convenient restriction sites. Ifsuch sites do not exist, the synthetic oligonucleotide adaptors orlinkers are used in accordance with conventional practice. Thetranscriptional and translational regulatory nucleic acid will generallybe appropriate to the host cell used to express the fusion protein; forexample, transcriptional and translational regulatory nucleic acidsequences from Bacillus are preferably used to express the fusionprotein in Bacillus. Numerous types of appropriate expression vectors,and suitable regulatory sequences are known in the art for a variety ofhost cells.

In general, the transcriptional and translational regulatory sequencesmay include, but are not limited to, promoter sequences, ribosomalbinding sites, transcriptional start and stop sequences, translationalstart and stop sequences, and enhancer or activator sequences. In apreferred embodiment, the regulatory sequences include a promoter andtranscriptional start and stop sequences.

Promoter sequences encode either constitutive or inducible promoters.The promoters may be either naturally occurring promoters or hybridpromoters. Hybrid promoters, which combine elements of more than onepromoter, are also known in the art, and are useful in the presentinvention. In a preferred embodiment, the promoters are strongpromoters, allowing high expression in cells, particularly mammaliancells, such as the CMV promoter, particularly in combination with a Tetregulatory element.

In addition, the expression vector may comprise additional elements. Forexample, the expression vector may have two replication systems, thusallowing it to be maintained in two organisms, for example in mammalianor insect cells for expression and in a prokaryotic host for cloning andamplification. Furthermore, for integrating expression vectors, theexpression vector contains at least one sequence homologous to the hostcell genome, and preferably two homologous sequences which flank theexpression construct. The integrating vector may be directed to aspecific locus in the host cell by selecting the appropriate homologoussequence for inclusion in the vector. Constructs for integrating vectorsare well known in the art.

In addition, in a preferred embodiment, the expression vector contains aselectable marker gene to allow the selection of transformed host cells.Selection genes are well known in the art and will vary with the hostcell used.

A preferred expression vector system is a retroviral vector system suchas is generally described in PCT/US97/01019 and PCT/US97101048, both ofwhich are hereby expressly incorporated by reference.

In a preferred embodiment, the expression vector comprises thecomponents described above and a gene encoding a variant TNF-α protein.As will be appreciated by those in the art, all combinations arepossible and accordingly, as used herein, the combination of components,comprised by one or more vectors, which may be retroviral or not, isreferred to herein as a “vector composition”.

The variant TNF-α nucleic acids are introduced into the cells eitheralone or in combination with an expression vector. By “introduced into”or grammatical equivalents herein is meant that the nucleic acids enterthe cells in a manner suitable for subsequent expression of the nucleicacid. The method of introduction is largely dictated by the targetedcell type, discussed below. Exemplary methods include CaPO₄precipitation, liposome fusion, lipofectin®, electroporation, viralinfection, etc. The variant TNF-α nucleic acids may stably integrateinto the genome of the host cell (for example, with retroviralintroduction, outlined below), or may exist either transiently or stablyin the cytoplasm (i.e. through the use of traditional plasmids,utilizing standard regulatory sequences, selection markers, etc.).

The variant TNF-α proteins of the present invention are produced byculturing a host cell transformed with an expression vector containingnucleic acid encoding a variant TNF-α protein, under the appropriateconditions to induce or cause expression of the variant TNF-α protein.The conditions appropriate for variant TNF-α protein expression willvary with the choice of the expression vector and the host cell, andwill be easily ascertained by one skilled in the art through routineexperimentation. For example, the use of constitutive promoters in theexpression vector will require optimizing the growth and proliferationof the host cell, while the use of an inducible promoter requires theappropriate growth conditions for induction. In addition, in someembodiments, the timing of the harvest is important. For example, thebaculoviral systems used in insect cell expression are lytic viruses,and thus harvest time selection can be crucial for product yield.

Appropriate host cells include yeast, bacteria, archebacteria, fungi,and insect and animal cells, including mammalian cells. Of particularinterest are Drosophila melangaster cells, Saccharomyces cerevisiae andother yeasts, E. coli, Bacillus subtilis, SF9 cells, C129 cells, 293cells, Neurospora, BHK, CHO, COS, Pichia Pastoris, etc.

In a preferred embodiment, the variant TNF-α proteins are expressed inmammalian cells. Mammalian expression systems are also known in the art,and include retroviral systems. A mammalian promoter is any DNA sequencecapable of binding mammalian RNA polymerase and initiating thedownstream (3′) transcription of a coding sequence for the fusionprotein into mRNA. A promoter will have a transcription initiatingregion, which is usually placed proximal to the 5′ end of the codingsequence, and a TATA box, using a located 25–30 base pairs upstream ofthe transcription initiation site. The TATA box is thought to direct RNApolymerase II to begin RNA synthesis at the correct site. A mammalianpromoter will also contain an upstream promoter element (enhancerelement), typically located within 100 to 200 base pairs upstream of theTATA box. An upstream promoter element determines the rate at whichtranscription is initiated and can act in either orientation. Ofparticular use as mammalian promoters are the promoters from mammalianviral genes, since the viral genes are often highly expressed and have abroad host range. Examples include the SV40 early promoter, mousemammary tumor virus LTR promoter, adenovirus major late promoter, herpessimplex virus promoter, and the CMV promoter.

Typically, transcription termination and polyadenylation sequencesrecognized by mammalian cells are regulatory regions located 3′ to thetranslation stop codon and thus, together with the promoter elements,flank the coding sequence. The 3′ terminus of the mature mRNA is formedby site-specific post-translational cleavage and polyadenylation.Examples of transcription terminator and polyadenlytion signals includethose derived form SV40.

The methods of introducing exogenous nucleic acid into mammalian hosts,as well as other hosts, is well known in the art, and will vary with thehost cell used. Techniques include dextran-mediated transfection,calcium phosphate precipitation, polybrene mediated transfection,protoplast fusion, electroporation, viral infection, encapsulation ofthe polynucleotide(s) in liposomes, and direct microinjection of the DNAinto nuclei. As outlined herein, a particularly preferred methodutilizes retroviral infection, as outlined in PCT US97/01019,incorporated by reference.

As will be appreciated by those in the art, the type of mammalian cellsused in the present invention can vary widely. Basically, any mammaliancells may be used, with mouse, rat, primate and human cells beingparticularly preferred, although as will be appreciated by those in theart, modifications of the system by pseudotyping allows all eukaryoticcells to be used, preferably higher eukaryotes. As is more fullydescribed below, a screen will be set up such that the cells exhibit aselectable phenotype in the presence of a bioactive peptide. As is morefully described below, cell types implicated in a wide variety ofdisease conditions are particularly useful, so long as a suitable screenmay be designed to allow the selection of cells that exhibit an alteredphenotype as a consequence of the presence of a peptide within the cell.

Accordingly, suitable cell types include, but are not limited to, tumorcells of all types (particularly melanoma, myeloid leukemia, carcinomasof the lung, breast, ovaries, colon, kidney, prostate, pancreas andtestes), cardiomyocytes, endothelial cells, epithelial cells,lymphocytes (T-cell and B cell), mast cells, eosinophils, vascularintimal cells, hepatocytes, leukocytes including mononuclear leukocytes,stem cells such as haemopoetic, neural, skin, lung, kidney, liver andmyocyte stem cells (for use in screening for differentiation andde-differentiation factors), osteoclasts, chondrocytes and otherconnective tissue cells, keratinocytes, melanocytes, liver cells, kidneycells, and adipocytes. Suitable cells also include known research cells,including, but not limited to, Jurkat T cells, NIH3T3 cells, CHO, Cos,etc. See the ATCC cell line catalog, hereby expressly incorporated byreference.

In one embodiment, the cells may be additionally genetically engineered,that is, contain exogeneous nucleic acid other than the variant TNF-αnucleic acid.

In a preferred embodiment, the variant TNF-α proteins are expressed inbacterial systems. Bacterial expression systems are well known in theart.

A suitable bacterial promoter is any nucleic acid sequence capable ofbinding bacterial RNA polymerase and initiating the downstream (3′)transcription of the coding sequence of the variant TNF-α protein intomRNA. A bacterial promoter has a transcription initiation region whichis usually placed proximal to the 5′ end of the coding sequence. Thistranscription initiation region typically includes an RNA polymerasebinding site and a transcription initiation site. Sequences encodingmetabolic pathway enzymes provide particularly useful promotersequences. Examples include promoter sequences derived from sugarmetabolizing enzymes, such as galactose, lactose and maltose, andsequences derived from biosynthetic enzymes such as tryptophan.Promoters from bacteriophage may also be used and are known in the art.In addition, synthetic promoters and hybrid promoters are also useful;for example, the tac promoter is a hybrid of the trp and lac promotersequences. Furthermore, a bacterial promoter can include naturallyoccurring promoters of non-bacterial origin that have the ability tobind bacterial RNA polymerase and initiate transcription.

In addition to a functioning promoter sequence, an efficient ribosomebinding site is desirable. In E. coli, the ribosome binding site iscalled the Shine-Delgarno (SD) sequence and includes an initiation codonand a sequence 3–9 nucleotides in length located 3–11 nucleotidesupstream of the initiation codon.

The expression vector may also include a signal peptide sequence thatprovides for secretion of the variant TNF-α protein in bacteria. Thesignal sequence typically encodes a signal peptide comprised ofhydrophobic amino acids which direct the secretion of the protein fromthe cell, as is well known in the art. The protein is either secretedinto the growth media (gram-positive bacteria) or into the periplasmicspace, located between the inner and outer membrane of the cell(gram-negative bacteria). For expression in bacteria, usually bacterialsecretory leader sequences, operably linked to a variant TNF-α encodingnucleic acid, are preferred.

The bacterial expression vector may also include a selectable markergene to allow for the selection of bacterial strains that have beentransformed. Suitable selection genes include genes which render thebacteria resistant to drugs such as ampicillin, chloramphenicol,erythromycin, kanamycin, neomycin and tetracycline. Selectable markersalso include biosynthetic genes, such as those in the histidine,tryptophan and leucine biosynthetic pathways.

These components are assembled into expression vectors. Expressionvectors for bacteria are well known in the art, and include vectors forBacillus subtilis, E. coli, Streptococcus cremoris, and Streptococcuslividans, among others.

The bacterial expression vectors are transformed into bacterial hostcells using techniques well known in the art, such as calcium chloridetreatment, electroporation, and others.

In one embodiment, variant TNF-α proteins are produced in insect cells.Expression vectors for the transformation of insect cells, and inparticular, baculovirus-based expression vectors, are well known in theart.

In a preferred embodiment, variant TNF-α protein is produced in yeastcells. Yeast expression systems are well known in the art, and includeexpression vectors for Saccharomyces cerevisiae, Candida albicans and C.maltosa, Hansenula polymorpha, Kluyveromyces fragilis and K. lactis,Pichia guillerimondii and P. pastoris, Schizosaccharomyces pombe, andYarrowia lipolytica. Preferred promoter sequences for expression inyeast include the inducible GAL1, 10 promoter, the promoters fromalcohol dehydrogenase, enolase, glucokinase, glucose-6-phosphateisomerase, glyceraldehyde-3-phosphate-dehydrogenase, hexokinase,phosphofructokinase, 3-phosphoglycerate mutase, pyruvate kinase, and theacid phosphatase gene. Yeast selectable markers include ADE2, HIS4,LEU2, TRP1, and ALG7, which confers resistance to tunicamycin; theneomycin phosphotransferase gene, which confers resistance to G418; andthe CUP1 gene, which allows yeast to grow in the presence of copperions.

In addition, the variant TNF-α polypeptides of the invention may befurther fused to other proteins, if desired, for example to increaseexpression or stabilize the protein.

In one embodiment, the variant TNF-α nucleic acids, proteins andantibodies of the invention are labeled with a label other than thescaffold. By “labeled” herein is meant that a compound has at least oneelement, isotope or chemical compound attached to enable the detectionof the compound. In general, labels fall into three classes: a) isotopiclabels, which may be radioactive or heavy isotopes; b) immune labels,which may be antibodies or antigens; and c) colored or fluorescent dyes.The labels may be incorporated into the compound at any position.

Once made, the variant TNF-α proteins may be covalently modified.Covalent and non-covalent modifications of the protein are thus includedwithin the scope of the present invention. Such modifications may beintroduced into a variant TNF-α polypeptide by reacting targeted aminoacid residues of the polypeptide with an organic derivatizing agent thatis capable of reacting with selected side chains or terminal residues.

One type of covalent modification includes reacting targeted amino acidresidues of a variant TNF-α polypeptide with an organic derivatizingagent that is capable of reacting with selected side chains or the N-orC-terminal residues of a variant TNF-α polypeptide. Derivatization withbifunctional agents is useful, for instance, for crosslinking a variantTNF-α protein to a water-insoluble support matrix or surface for use inthe method for purifying anti-variant TNF-α antibodies or screeningassays, as is more fully described below. Commonly used crosslinkingagents include, e.g., 1,1-bis(diazoacetyl)-2-phenylethane,glutaraldehyde, N-hydroxysuccinimide esters, for example, esters with4-azidosalicylic acid, homobifunctional imidoesters, includingdisuccinimidyl esters such as 3,3′-dithiobis(succinimidyl-propionate),bifunctional maleimides such as bis-N-maleimido-1,8-octane and agentssuch as methyl-3-[(p-azidophenyl)dithio]propioimidate.

Other modifications include deamidation of glutaminyl and asparaginylresidues to the corresponding glutamyl and aspartyl residues,respectively, hydroxylation of proline and lysine, phosphorylation ofhydroxyl groups of seryl or threonyl residues, methylation of the“-amino groups of lysine, arginine, and histidine side chains [T. E.Creighton, Proteins: Structure and Molecular Properties, W. H. Freeman &Co., San Francisco, pp. 79–86 (1983)], acetylation of the N-terminalamine, and amidation of any C-terminal carboxyl group.

Another type of covalent modification of the variant TNF-α polypeptideincluded within the scope of this invention comprises altering thenative glycosylation pattern of the polypeptide. “Altering the nativeglycosylation pattern” is intended for purposes herein to mean deletingone or more carbohydrate moieties found in native sequence variant TNF-αpolypeptide, andfor adding one or more glycosylation sites that are notpresent in the native sequence variant TNF-α polypeptide.

Addition of glycosylation sites to variant TNF-α polypeptides may beaccomplished by altering the amino acid sequence thereof. The alterationmay be made, for example, by the addition of, or substitution by, one ormore serine or threonine residues to the native sequence variant TNF-αpolypeptide (for O-linked glycosylation sites). The variant TNF-α aminoacid sequence may optionally be altered through changes at the DNAlevel, particularly by mutating the DNA encoding the variant TNF-αpolypeptide at preselected bases such that codons are generated thatwill translate into the desired amino acids.

Another means of increasing the number of carbohydrate moieties on thevariant TNF-α polypeptide is by chemical or enzymatic coupling ofglycosides to the polypeptide. Such methods are described in the art,e.g., in WO 87/05330 published Sep. 11, 1987, and in Aplin and Wriston,CRC Crit. Rev. Biochem., pp. 259–306 (1981).

Removal of carbohydrate moieties present on the variant TNF-αpolypeptide may be accomplished chemically or enzymatically or bymutational substitution of codons encoding for amino acid residues thatserve as targets for glycosylation. Chemical deglycosylation techniquesare known in the art and described, for instance, by Hakimuddin, et al.,Arch. Biochem. Biophys., 259:52 (1987) and by Edge et al., Anal.Biochem., 118:131 (1981). Enzymatic cleavage of carbohydrate moieties onpolypeptides can be achieved by the use of a variety of endo-andexo-glycosidases as described by Thotakura et al., Meth. Enzymol.,138:350 (1987).

Such derivatized moieties may improve the solubility, absorption,permeability across the blood brain barrier biological half life, andthe like. Such moieties or modifications of variant TNF-α polypeptidesmay alternatively eliminate or attenuate any possible undesirable sideeffect of the protein and the like. Moieties capable of mediating sucheffects are disclosed, for example, in Remington's PharmaceuticalSciences, 16th ed., Mack Publishing Co., Easton, Pa. (1980).

Another type of covalent modification of variant TNF-α comprises linkingthe variant TNF-α polypeptide to one of a variety of nonproteinaceouspolymers, e.g., polyethylene glycol, polypropylene glycol, orpolyoxyalkylenes, in the manner set forth in U.S. Pat. Nos. 4,640,835;4,496,689; 4,301,144; 4,670,417; 4,791,192 or 4,179,337.

Variant TNF-α polypeptides of the present invention may also be modifiedin a way to form chimeric molecules comprising a variant TNF-αpolypeptide fused to another, heterologous polypeptide or amino acidsequence. In one embodiment, such a chimeric molecule comprises a fusionof a variant TNF-α polypeptide with a tag polypeptide which provides anepitope to which an anti-tag antibody can selectively bind. The epitopetag is generally placed at the amino-or carboxyl-terminus of the variantTNF-α polypeptide. The presence of such epitope-tagged forms of avariant TNF-α polypeptide can be detected using an antibody against thetag polypeptide. Also, provision of the epitope tag enables the variantTNF-α polypeptide to be readily purified by affinity purification usingan anti-tag antibody or another type of affinity matrix that binds tothe epitope tag. In an alternative embodiment, the chimeric molecule maycomprise a fusion of a variant TNF-α polypeptide with an immunoglobulinor a particular region of an immunoglobulin. For a bivalent form of thechimeric molecule, such a fusion could be to the Fc region of an IgGmolecule.

Various tag polypeptides and their respective antibodies are well knownin the art. Examples include poly-histidine (poly-his) orpoly-histidine-glycine (poly-his-gly) tags; the flu HA tag polypeptideand its antibody 12CA5 [Field et al., Mol. Cell. Biol. 8:2159–2165(1988)]; the c-myc tag and the 8F9, 3C7, 6E10, G4, B7 and 9E10antibodies thereto [Evan et al., Molecular and Cellular Biology,5:3610–3616 (1985)]; and the Herpes Simplex virus glycoprotein D (gD)tag and its antibody [Paborsky et al., Protein Engineering, 3(6):547–553(1990)]. Other tag polypeptides include the Flag-peptide [Hopp et al.,BioTechnology 6:1204–1210 (1988)]; the KT3 epitope peptide [Martin etal., Science 255:192–194 (1992)]; tubulin epitope peptide [Skinner etal., J. Biol. Chem. 266:15163–15166 (1991)]; and the T7 gene 10 proteinpeptide tag [Lutz-Freyermuth et al., Proc. Natl. Acad. Sci. U.S.A.87:6393–6397 (1990)].

In a preferred embodiment, the variant TNF-α protein is purified orisolated after expression. Variant TNF-α proteins may be isolated orpurified in a variety of ways known to those skilled in the artdepending on what other components are present in the sample. Standardpurification methods include electrophoretic, molecular, immunologicaland chromatographic techniques, including ion exchange, hydrophobic,affinity, and reverse-phase HPLC chromatography, and chromatofocusing.For example, the variant TNF-α protein may be purified using a standardanti-library antibody column. Ultrafiltration and diafiltrationtechniques, in conjunction with protein concentration, are also useful.For general guidance in suitable purification techniques, see Scopes,R., Protein Purification, Springer-Verlag, N.Y. (1982). The degree ofpurification necessary will vary depending on the use of the variantTNF-α protein. In some instances no purification will be necessary.

Once made, the variant TNF-α proteins and nucleic acids of the inventionfind use in a number of applications. In a preferred embodiment, thevariant TNF-α proteins are administered to a patient to treat an TNF-αrelated disorder.

By “TNF-α related disorder” or “TNF-α responsive disorder” or“condition” herein is meant a disorder that can be ameliorated by theadministration of a pharmaceutical composition comprising a variantTNF-α protein, including, but not limited to, inflammatory andimmunological disorders. In a preferred embodiment, the variant TNF-αprotein is used to treat rheumatoid arthritis.

In a preferred embodiment, a therapeutically effective dose of a variantTNF-α protein is administered to a patient in need of treatment. By“therapeutically effective dose” herein is meant a dose that producesthe effects for which it is administered. The exact dose will depend onthe purpose of the treatment, and will be ascertainable by one skilledin the art using known techniques. In a preferred embodiment, dosages ofabout 5 pg/kg are used, administered either intraveneously orsubcutaneously. As is known in the art, adjustments for variant TNF-αprotein degradation, systemic versus localized delivery, and rate of newprotease synthesis, as well as the age, body weight, general health,sex, diet, time of administration, drug interaction and the severity ofthe condition may be necessary, and will be ascertainable with routineexperimentation by those skilled in the art.

A “patient” for the purposes of the present invention includes bothhumans and other animals, particularly mammals, and organisms. Thus themethods are applicable to both human therapy and veterinaryapplications. In the preferred embodiment the patient is a mammal, andin the most preferred embodiment the patient is human.

The term “treatment” in the instant invention is meant to includetherapeutic treatment, as well as prophylactic, or suppressive measuresfor the disease or disorder. Thus, for example, successfuladministration of a variant TNF-α protein prior to onset of the diseaseresults in “treatment” of the disease. As another example, successfuladministration of a variant TNF-α protein after clinical manifestationof the disease to combat the symptoms of the disease comprises“treatment” of the disease. “Treatment” also encompasses administrationof a variant TNF-α protein after the appearance of the disease in orderto eradicate the disease. Successful administration of an agent afteronset and after clinical symptoms have developed, with possibleabatement of clinical symptoms and perhaps amelioration of the disease,comprises “treatment” of the disease.

Those “in need of treatment” include mammals already having the diseaseor disorder, as well as those prone to having the disease or disorder,including those in which the disease or disorder is to be prevented.

In another embodiment, a therapeutically effective dose of a variantTNF-α protein, a variant TNF-α gene, or a variant TNF-α antibody isadministered to a patient having a disease involving inappropriateexpression of TNF-α. A “disease involving inappropriate expression of atTNF-α” within the scope of the present invention is meant to includediseases or disorders characterized by aberrant TNF-α, either byalterations in the amount of TNF-α present or due to the presence ofmutant TNF-α. An overabundance may be due to any cause, including, butnot limited to, overexpression at the molecular level, prolonged oraccumulated appearance at the site of action, or increased activity ofTNF-α relative to normal. Included within this definition are diseasesor disorders characterized by a reduction of TNF-α. This reduction maybe due to any cause, including, but not limited to, reduced expressionat the molecular level, shortened or reduced appearance at the site ofaction, mutant forms of TNF-α, or decreased activity of TNF-α relativeto normal. Such an overabundance or reduction of TNF-α can be measuredrelative to normal expression, appearance, or activity of TNF-αaccording to, but not limited to, the assays described and referencedherein.

The administration of the variant TNF-α proteins of the presentinvention, preferably in the form of a sterile aqueous solution, can bedone in a variety of ways, including, but not limited to, orally,subcutaneously, intravenously, intranasally, transdermally,intraperitoneally, intramuscularly, intrapulmonary, vaginally, rectally,or intraocularly. In some instances, for example, in the treatment ofwounds, inflammation, etc., the variant TNF-α protein may be directlyapplied as a solution or spray. Depending upon the manner ofintroduction, the pharmaceutical composition may be formulated in avariety of ways. The concentration of the therapeutically active variantTNF-α protein in the formulation may vary from about 0.1 to 100 weight%. In another preferred embodiment, the concentration of the variantTNF-α protein is in the range of 0.003 to 1.0 molar, with dosages from0.03, 0.05, 0.1, 0.2, and 0.3 millimoles per kilogram of body weightbeing preferred.

The pharmaceutical compositions of the present invention comprise avariant TNF-α protein in a form suitable for administration to apatient. In the preferred embodiment, the pharmaceutical compositionsare in a water soluble form, such as being present as pharmaceuticallyacceptable salts, which is meant to include both acid and base additionsalts. “Pharmaceutically acceptable acid addition salt” refers to thosesalts that retain the biological effectiveness of the free bases andthat are not biologically or otherwise undesirable, formed withinorganic acids such as hydrochloric acid, hydrobromic acid, sulfuricacid, nitric acid, phosphoric acid and the like, and organic acids suchas acetic acid, propionic acid, glycolic acid, pyruvic acid, oxalicacid, maleic acid, malonic acid, succinic acid, fumaric acid, tartaricacid, citric acid, benzoic acid, cinnamic acid, mandelic acid,methanesulfonic acid, ethanesulfonic acid, p-toluenesulfonic acid,salicylic acid and the like. “Pharmaceutically acceptable base additionsalts” include those derived from inorganic bases such as sodium,potassium, lithium, ammonium, calcium, magnesium, iron, zinc, copper,manganese, aluminum salts and the like. Particularly preferred are theammonium, potassium, sodium, calcium, and magnesium salts. Salts derivedfrom pharmaceutically acceptable organic non-toxic bases include saltsof primary, secondary, and tertiary amines, substituted amines includingnaturally occurring substituted amines, cyclic amines and basic ionexchange resins, such as isopropylamine, trimethylamine, diethylamine,triethylamine, tripropylamine, and ethanolamine.

The pharmaceutical compositions may also include one or more of thefollowing: carrier proteins such as serum albumin; buffers such asNaOAc; fillers such as microcrystalline cellulose, lactose, corn andother starches; binding agents; sweeteners and other flavoring agents;coloring agents; and polyethylene glycol. Additives are well known inthe art, and are used in a variety of formulations.

In a further embodiment, the variant TNF-α proteins are added in amicellular formulation; see U.S. Pat. No.5,833,948, hereby expresslyincorporated by reference in its entirety.

Combinations of pharmaceutical compositions may be administered.Moreover, the compositions may be administered in combination with othertherapeutics.

In one embodiment provided herein, antibodies, including but not limitedto monoclonal and polyclonal antibodies, are raised against variantTNF-α proteins using methods known in the art. In a preferredembodiment, these anti-variant TNF-α antibodies are used forimmunotherapy. Thus, methods of immunotherapy are provided. By“immunotherapy” is meant treatment of an TNFa related disorders with anantibody raised against a variant TNF-α protein. As used herein,immunotherapy can be passive or active. Passive immunotherapy, asdefined herein, is the passive transfer of antibody to a recipient(patient). Active immunization is the induction of antibody and/orT-cell responses in a recipient (patient). Induction of an immuneresponse can be the consequence of providing the recipient with avariant TNF-α protein antigen to which antibodies are raised. Asappreciated by one of ordinary skill in the art, the variant TNF-αprotein antigen may be provided by injecting a variant TNF-α polypeptideagainst which antibodies are desired to be raised into a recipient, orcontacting the recipient with a variant TNF-α protein encoding nucleicacid, capable of expressing the variant TNF-α protein antigen, underconditions for expression of the variant TNF-α protein antigen.

In another preferred embodiment, a therapeutic compound is conjugated toan antibody, preferably an anti-variant TNF-α protein antibody. Thetherapeutic compound may be a cytotoxic agent. In this method, targetingthe cytotoxic agent to tumor tissue or cells, results in a reduction inthe number of afflicted cells, thereby reducing symptoms associated withcancer, and variant TNF-α protein related disorders. Cytotoxic agentsare numerous and varied and include, but are not limited to, cytotoxicdrugs or toxins or active fragments of such toxins. Suitable toxins andtheir corresponding fragments include diptheria A chain, exotoxin Achain, ricin A chain, abrin A chain, curcin, crotin, phenomycin,enomycin and the like. Cytotoxic agents also include radiochemicals madeby conjugating radioisotopes to antibodies raised against cell cycleproteins, or binding of a radionuclide to a chelating agent that hasbeen covalently attached to the antibody.

In a preferred embodiment, variant TNF-α proteins are administered astherapeutic agents, and can be formulated as outlined above. Similarly,variant TNF-α genes (including both the full-length sequence, partialsequences, or regulatory sequences of the variant TNF-α coding regions)can be administered in gene therapy applications, as is known in theart. These variant TNF-α genes can include antisense applications,either as gene therapy (i.e. for incorporation into the genome) or asantisense compositions, as will be appreciated by those in the art.

In a preferred embodiment, the nucleic acid encoding the variant TNF-αproteins may also be used in gene therapy. In gene therapy applications,genes are introduced into cells in order to achieve in vivo synthesis ofa therapeutically effective genetic product, for example for replacementof a defective gene. “Gene therapy” includes both conventional genetherapy where a lasting effect is achieved by a single treatment, andthe administration of gene therapeutic agents, which involves the onetime or repeated administration of a therapeutically effective DNA ormRNA. Antisense RNAs and DNAs can be used as therapeutic agents forblocking the expression of certain genes in vivo. It has already beenshown that short antisense oligonucleotides can be imported into cellswhere they act as inhibitors, despite their low intracellularconcentrations caused by their restricted uptake by the cell membrane.[Zamecnik et al., Proc. Natl. Acad. Sci. U.S.A. 83:4143–4146 (1986)].The oligonucleotides can be modified to enhance their uptake, e.g. bysubstituting their negatively charged phosphodiester groups by unchargedgroups.

There are a variety of techniques available for introducing nucleicacids into viable cells. The techniques vary depending upon whether thenucleic acid is transferred into cultured cells in vitro, or in vivo inthe cells of the intended host. Techniques suitable for the transfer ofnucleic acid into mammalian cells in vitro include the use of liposomes,electroporation, microinjection, cell fusion, DEAE-dextran, the calciumphosphate precipitation method, etc. The currently preferred in vivogene transfer techniques include transfection with viral (typicallyretroviral) vectors and viral coat protein-liposome mediatedtransfection [Dzau et al., Trends in Biotechnology 11:205–210 (1993)].In some situations it is desirable to provide the nucleic acid sourcewith an agent that targets the target cells, such as an antibodyspecific for a cell surface membrane protein or the target cell, aligand for a receptor on the target cell, etc. Where liposomes areemployed, proteins which bind to a cell surface membrane proteinassociated with endocytosis may be used for targeting and/or tofacilitate uptake, e.g. capsid proteins or fragments thereof tropic fora particular cell type, antibodies for proteins which undergointernalization in cycling, proteins that target intracellularlocalization and enhance intracellular half-life. The technique ofreceptor-mediated endocytosis is described, for example, by Wu et al.,J. Biol. Chem. 262:4429–4432 (1987); and Wagner et al., Proc. Nati.Acad. Sci. U.S.A. 87:3410–3414 (1990). For review of gene marking andgene therapy protocols see Anderson et al., Science 256:808–813 (1992).

In a preferred embodiment, variant TNF-α genes are administered as DNAvaccines, either single genes or combinations of variant TNF-α genes.Naked DNA vaccines are generally known in the art. Brower, NatureBiotechnology, 16:1304–1305 (1998). Methods for the use of genes as DNAvaccines are well known to one of ordinary skill in the art, and includeplacing a variant TNF-α gene or portion of a variant TNF-α gene underthe control of a promoter for expression in a patient in need oftreatment. The variant TNF-α gene used for DNA vaccines can encodefull-length variant TNF-α proteins, but more preferably encodes portionsof the variant TNF-α proteins including peptides derived from thevariant TNF-α protein. In a preferred embodiment a patient is immunizedwith a DNA vaccine comprising a plurality of nucleotide sequencesderived from a variant TNF-α gene. Similarly, it is possible to immunizea patient with a plurality of variant TNF-α genes or portions thereof asdefined herein. Without being bound by theory, expression of thepolypeptide encoded by the DNA vaccine, cytotoxic T-cells, helperT-cells and antibodies are induced which recognize and destroy oreliminate cells expressing TNF-α proteins.

In a preferred embodiment, the DNA vaccines include a gene encoding anadjuvant molecule with the DNA vaccine. Such adjuvant molecules includecytokines that increase the immunogenic response to the variant TNF-αpolypeptide encoded by the DNA vaccine. Additional or alternativeadjuvants are known to those of ordinary skill in the art and find usein the invention.

All references cited herein are incorporated by reference in theirentirety.

EXAMPLES Example 1 Protocol for TNFα Library Expression and Purification

Methods:

-   1) Overnight culture preparation:

Competent Tuner(DE3)pLysS cells in 96 well-PCR plates were transformedwith 1 ul of TNFa library DNAs and spread on LB agar plates with 34μg/ml chloramphenicol and 100 μg/ml ampicillin. After an overnightgrowth at 37° C., a colony was picked from each plate in 1.5 ml of CGmedia with 34 μg/ml chloramphenicol and 100 μg/ml ampicilline kept in 96deep well block. The block was shaken at 250 rpm at 37° C. overnight.

-   2) Expression:

Colonies were picked from the plate into 5 ml CG media (34 μg/mlchloramphenicol and 100 μg/ml ampicillin) in 24-well block and grown at37° C. at 250 rpm until OD600 0.6 were reached, at which time IPTG wasadded to each well to 1 μM concentration. The culture was grown 4 extrahours

-   3) Lysis:

The 24-well block was centrifuged at 3000 rpm for 10 minutes. Thepellets were resuspended in 700 ul of lysis buffer (50 mM NaH₂PO₄, 300mM NaCl, 10 mM imidazole). After freezing at −80° C. for 2 minutes andthawing at 37° C. twice, MgCl₂ was added to 10 mM, and DNase 1 to 75μg/ml. The mixure was incubated at 37° C. for 30 minutes.

-   4) Ni NTA column purification:

Purification was carried out following Qiagen Ni NTA spin columnpurification protocol for native condition. The purified protein wasdialyzed against 1 X PBS for 1 hour at 4° C. four times. Dialyzedprotein was filter sterilized, using Millipore multiscreenGV filterplate to allow the addition of protein to the sterile mammalian cellculture assay later on.

-   5) Quantification:

Purified protein was quantified by SDS PAGE, followed by Coomassiestain, and by Kodak digital image densitometry.

-   6) TNF-α Activity Assay assay:

The activity of variant TNF-α protein samples was tested using VybrantAssay Kit and Caspase Assay kit. Sytox Green nucleic acid stain is usedto detect TNF-induced cell permeability in Actinomycin-D sensitized cellline. Upon binding to cellular nucleic acids, the stain exhibits a largefluorescence enhancement, which is then measured. This stain is excludedfrom live cells but penetrates cells with compromised membranes.

Caspase assay is a fluorimetric assay, which can differentiate betweenapoptosis and necrosis in the cells. This kit measures the caspaseactivity, triggered during apoptosis of the cells.

-   A) Materials:    -   Cell Line: WEHI Var-13 Cell line from ATCC    -   Media: RPMI Complete media with 10% FBS.    -   Vybrant TNF Kit: Cat #V-23100; Molecular Probes        -   Kit contains SYTOX Green nucleic acid stain (500 mM            solution)        -   and Actinomycin D (1 mg/mL)    -   Caspase Assay Kit: Cat #3 005 372; Roche        -   Kit contains substrate stock solution (500 uM) and            incubation buffer    -   TNF-α Standard stock: 10 ug/mL stock of h-TNF-α from R & D    -   Unknown Samples: In house TNF-α library samples    -   96-well Plates: 1 mL deep well and 250 uL wells    -   Micro plate Reader-   B) Method:

Plate WEHI cells at 2.5×10⁵ cells/mL, 24 hrs prior to the assay; (100μL/well for the Sytox assay and 50 μL/well for the Caspase assay).

On the day of the experiment, prepare assay media as follows:

-   1) Assay Media for Sytox Assay (1X): Prepare assay medium by    diluting the concentrated Sytox Green stain and the concentrated    actinomycin D solution 500-fold into RPMI, to a final concentration    of 10 μM Sytox and 2 μg/mL actinomycin D.    -   10 mL complete RPMI medium    -   20 μL SYTOX Green    -   20 μL actinomycin D-   2) Prepare Assay Media for Caspase Assay (1X):    -   10 mL complete RPMI medium    -   20 uL Actinomycin D (2 μg/mL final conc.)-   3) Prepare Assay Media for samples: Sytox Assay (2X):    -   14 mL complete RPMI medium    -   56 μL SYTOX Green Nuclei acid stain    -   56 μL actinomycin D-   4) Prepare Assay Media: (2X): For samples: Caspase assay    -   14 mL complete RPMI medium    -   56 μL actinomycin D-   5) Set up and Run a Standard Curve Dilution:    -   TNF-α Std. stock: 10 μg/mL    -   Dilute to 1 ug/mL: 10 μL stock+90 μL Assay medium.

1X Assay medium for Final Conc. Sytox and Caspase Conc. in of TNF-αStock (uL) (μL) dilution plate on cells  10 uL of 1 μg 990  10 ng/mL   5ng/mL  5 uL of 1 μg 995  5 ng/mL  2.5 ng/mL 200 uL of 5 ng 300  2 ng/mL  1 ng/mL 100 uL of 5 ng 400  1 ng/mL  0.5 ng/mL 100 uL of 5 ng 900 500pg/mL  250 pg/mL 200 uL of 500 pg 300 200 pg/mL  100 pg/mL 100 uL of 500pg 400 100 pg/mL   50 pg/mL  50 uL of 500 pg 450  50 pg/mL   25 pg/mL 20 uL of 500 pg 480  20 pg/mL   10 pg/mL  10 uL of 500 pg 490  10 pg/mL  5 pg/mL  0 uL 500  0 pg/mL   0 pg/mL

For Unknown Samples: (Quantitated by Gel): TNF-α Library:

Normalize all the samples to the same starting concentration (500 ng/mL)as follows:

-   -   Neat: 500 ng/mL: 100 μL    -   1:10 of 500 ng=50 ng/mL: 20 μL neat+180 μL RPMI    -   1:10 of 50 ng=5 ng/mL 20 μL of 50 ng/mL+180 μL RPMI    -   1:10 of 5 ng/mL=0.5 ng/mL: 20 μL of 0.5 ng/mL+180 μL RPMI

-   6) For Sytox assay: On a separate dilution plate, add 60 μL of each    diluted sample to 60 μL of 2X Sytox assay media. Transfer 100 μL of    diluted samples to the cells cultured in 100 uL media. Incubate at    37° C. for 6 hrs. Read the plate using a fluorescence microplate    reader with filters appropriate for fluorescein (485 nm excitation    filter and 530 nm emission filter).

-   7) For Caspase assay: On a separate dilution plate, add 35 μL of    each diluted sample to 35 μL of 2X Caspase assay media. Transfer 50    μL of dil. Samples to the cells cultured in 50 μL media. Incubate at    37° C. for 4 hours. After 4 hrs. add Caspase Substrate (100 μL/well)    [Predilute substrate 1:10]. Incubate 2 more hrs. at 37° C. Read    (fluorescence).

-   C) Data Analysis:

The fluorescence signal is directly proportional to the number ofapoptotic cells. Plot fluorescence vs. TNF-α standard concentration tomake a standard curve. Compare the fluorescence obtained from thehighest point on the standard curve (5 ng/mL) to the fluorescenceobtained from the unknown samples, to determine the % activity of thesamples.

The data may be analyzed using a four-parameter fit program to determinethe 50% effective concentration for TNF (EC₅₀). % Activity of unknownsamples=(Fluor. Of unknown samples/fluor. of 5 ng/mL std. Point)×100.

Example 2 PDA Calculations for soluble TNF-R (p55)

Using publicly available protein three-dimensional structures for thep55 TNFR (Protein Data Bank codes 1 ext, 1 ncf, 1 tnr) both alone andcomplexed with its ligand, PDA can be used to design optimized solublep55 receptors as TNF-α antagonists. For the library shown below, thesequences shown were generated using PDA relative to the Protein DataBank 1 ext numbering scheme. Amino acid residues known from thestructure of the receptor-TNF-α complex to be critical for p55 bindingto TNF-α were designed around. The results shown in Table 1 are anexample of a library in which 15 position from the wild-type p55receptor were used for PDA design. Four of the positions chosen werenonpolar, 7 of the position were changed, and 4 were polar. The librarychown in Table 1 was pooled from five independent designs, and a 15%cutoff was ajpplied for each position in the library. The size of thelibrary for single mutation is 78 and the entire library is 1.5×10¹⁰sequences. The wild-type (WT) sequence (SEQ ID NO:9) is in the firstline of the table. The mutation pattern for soluble p55 receptors atgiven position is shown in the remainder of the table (SEQ ID NOS:10–22)

TABLE 1 SEQ ID NO: 55 56 57 59 62 65 67 68 69 70 95 97 98 101 103    9WT N H L H S K R K E M H W S L Q 10 V H L A A K V R A A K F S L I 11 T LK A K 12 E K E R R R D K M E T E F 13 D Q Q K H D H D H R Y 14 Q E W K15 N W R L E 16 R Y S W W 17 K F K N R 18 F F F T L T Q 19 K 20 Q 21 G Q22 S 23 H E Q

1. A recombinant nucleic acid encoding a variant TNF-α proteincomprising an amino acid substitution of amino acids 1–157 of SEQ ID NO:2, said substitution at a position selected from the group consisting ofpositions 21, 30, 31, 32, 35, 66, 84, 111, 112, 115, and 140, whereinsaid variant protein interacts with a human TNF-α protein to form amixed miner that has a reduced capacity to effect TNF-α receptorsignaling in a caspase assay.
 2. A recombinant nucleic acid according toclaim 1, wherein said variant TNF-α protein has between 2 and 5 aminoacid substitutions at positions selected from the group consisting ofpositions 21, 30, 31, 32, 35, 66, 84, 111, 112, 115, and
 140. 3. Arecombinant nucleic acid according to claim 1, wherein said substitutionis selected from the group consisting of K112D, Y115l, Y115T, end A84V.4. A vector composition comprising the recombinant nucleic acid ofclaim
 1. 5. A host cell comprising the recombinant nucleic acid ofclaim
 1. 6. A method of producing a protein composition comprisingculturing a host cell of claim 5 under conditions suitable for theexpression of said nucleic acids.
 7. A method of forming a TNF-αheterotrimer comprising: a) expressing a human TNF-α amino acid sequencecomprising the amino acid sequence of amino acids 1–157 of SEQ ID NO: 2;b) expressing a variant TNF-α amino acid sequence comprising an aminoacid substitution of amino acids 1–157 of SEQ ID NO: 2, saidsubstitution at a position selected from the group consisting ofpositions 21, 30, 31, 32, 33, 35, 65, 66, 67, 84, 111, 112, 115, 140,143, 144, 145, 146 and 147; wherein the TNF-α amino acid interacts withthe variant TNF-α sequence to form the TNF-α heterotrimer having areduced capacity to effect TNF-α receptor signaling in a caspase assay.8. The method according to claim 7, wherein said variant TNF-α sequencehas between 2 and 5 amino acid substitutions at positions selected fromthe group consisting of positions 21, 30, 31,32, 33, 35, 65, 66, 67, 84,111, 112, 115, 140, 143, 144, 145, 146 and
 147. 9. The method accordingto claim 7, wherein said substitution is selected from the groupconsisting of K112D, Y151l, Y115T, D143E, D143K, D143R, D143N, D143S,A145R A145K, A145E, E146K, E146R, and A84V.
 10. The method according toclaim 7, wherein said substitution comprises A145R.
 11. The methodaccording to claim 7, wherein said sequence comprises at least two aminoacid substitutions selected from the group consisting of A84V, D143S;A145E and E146K.
 12. The method according to claim 7, wherein said TNF-αheterotrimer comprises one said variant TNF-α sequence.
 13. The methodaccording to claim 7, wherein said TNF-α heterotrimer comprises two saidvariant TNF-α sequences.